Pandas .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Created by Berkay Alan**\n",
    "\n",
    "**Pandas**\n",
    "\n",
    "**28 of December, 2020**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Content\n",
    "\n",
    "- What is Pandas?\n",
    "- Importing Pandas Library\n",
    "- Pandas Series\n",
    "- Pandas Dataframes\n",
    "- Filtering\n",
    "- Adding/Removing rows and columns\n",
    "- Merging Dataframes\n",
    "- Sorting\n",
    "- Aggregation Functions\n",
    "- Grouping\n",
    "- Apply\n",
    "- Pivot Tables\n",
    "- Missing values(NaN)\n",
    "- Working external files in Pandas(csv,excel)\n",
    "- Exploring Netflix Dataset(basic)\n",
    "\n",
    "**Resources:** \n",
    "\n",
    "-  https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python\n",
    "\n",
    "-  https://www.kaggle.com/learn/pandas\n",
    "\n",
    "-  https://www.tutorialspoint.com/python_pandas/index.htm\n",
    "\n",
    "- https://www.analyticsvidhya.com/blog/2020/02/joins-in-pandas-master-the-different-types-of-joins-in-python/#:~:text=work%20using%20Pandas.-,Inner%20Join%20in%20Pandas,the%20intersection%20of%20two%20sets.\n",
    "\n",
    "- https://www.youtube.com/channel/UCmLF94o4Ts4YKkoUHq5491Q"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Pandas?"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas stands for “Python Data Analysis Library”.Pandas is a high-level Python data manipulation library developed by Wes McKinney in 2008. It is built on the Numpy package and its key data structure is called the DataFrame. DataFrames allow you to store and manipulate tabular data in rows of observations and columns of variables.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Pandas Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, you need yo download Pandas library. You can find it here: https://pandas.pydata.org/\n",
    "\n",
    "if you use Anaconda, you can download with conda run by using this command: conda install -c anaconda pandas\n",
    "\n",
    "Then, you need to import pandas in each notebook you want to use pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # As gives the naming as \"pd\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Pandas Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Pandas Series is like a column in a table. The difference between Numpy series and Pandas series is pandas series also hold indexes of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "3    40\n",
       "4    50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([10,20,30,40,50]) # It returns both indexes and values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "s= pd.Series([10,20,30,40,50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RangeIndex(start=0, stop=5, step=1)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.axes # Indexes of series can be seen by \"axes\" attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dtype #Variable Type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.size #Variable size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.ndim #One-dimensional array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 20, 30, 40, 50])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values #We can use \"values\" attribute if we want to see just values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(2) #We can reach first values by head() function. Specify number of rows you want to see. Default is 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    30\n",
       "3    40\n",
       "4    50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.tail(3) #We can reach last values by tail() function. Specify number of rows you want to see. Default is 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1] #Series can be indexed with selecting square brackets []. Indexes starts with 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing Index Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     13\n",
       "1    214\n",
       "2    210\n",
       "3    440\n",
       "4     53\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([13,214,210,440,53])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      13\n",
       "6     214\n",
       "7     210\n",
       "3     440\n",
       "87     53\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([13,214,210,440,53],index=[4,6,7,3,87]) #We can specify indexes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first      13\n",
       "second    214\n",
       "third     210\n",
       "fourth    440\n",
       "fifth      53\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series([13,214,210,440,53],index=[\"first\",\"second\",\"third\",\"fourth\",\"fifth\"]) #We can also specify string indexes.\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "210"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[\"third\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating series with key/value objects(Dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use a key/value object, like a dictionary, when creating a Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Jonh': 42, 'Julia': 53, 'Dan': 21}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages = {\"Jonh\": 42, \"Julia\": 53, \"Dan\": 21}\n",
    "ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jonh     42\n",
       "Julia    53\n",
       "Dan      21\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(ages) #We can create series with dictionaries.The keys of the dictionary become the labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to select only some of the items in the dictionary, use the index argument and specify only the items you want to include in the Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jonh    42\n",
       "Dan     21\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(ages,index=[\"Jonh\",\"Dan\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Pandas Series by Numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 24, 36, 47, 85])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([10,24,36,47,85])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    24\n",
       "2    36\n",
       "3    47\n",
       "4    85\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging 2 different series (Concat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = pd.Series([2,3,55,2,6,44])\n",
    "s2 = pd.Series([421,325,3426,2,1,4,42])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2\n",
       "1       3\n",
       "2      55\n",
       "3       2\n",
       "4       6\n",
       "5      44\n",
       "0     421\n",
       "1     325\n",
       "2    3426\n",
       "3       2\n",
       "4       1\n",
       "5       4\n",
       "6      42\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([s1,s2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selections in Pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2\n",
       "1     3\n",
       "2    55\n",
       "3     2\n",
       "4     6\n",
       "5    44\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([2,3,55,2,6,44])\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2\n",
       "1     3\n",
       "2    55\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     6\n",
       "5    44\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first      13\n",
       "second    214\n",
       "third     210\n",
       "fourth    440\n",
       "fifth      53\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series([13,214,210,440,53],index=[\"first\",\"second\",\"third\",\"fourth\",\"fifth\"]) #We can also specify string indexes.\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[\"first\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['first', 'second', 'third', 'fourth', 'fifth'], dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index #We can reach indexes of a serie with index attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.keys of first      13\n",
       "second    214\n",
       "third     210\n",
       "fourth    440\n",
       "fifth      53\n",
       "dtype: int64>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.keys #We can reach keys(values) of a serie with keys attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('first', 13),\n",
       " ('second', 214),\n",
       " ('third', 210),\n",
       " ('fourth', 440),\n",
       " ('fifth', 53)]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(s.items()) #We can see values and keys paired with items() function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Queries in Pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2\n",
       "1     3\n",
       "2    55\n",
       "3     2\n",
       "4     6\n",
       "5    44\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([2,3,55,2,6,44])\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 in s1 #Look if s1 contains 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "34 in s1 #Look if s1 contains 34"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fancy element Selecting in Pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2\n",
       "1     3\n",
       "2    55\n",
       "3     2\n",
       "4     6\n",
       "5    44\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([2,3,55,2,6,44])\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    55\n",
       "4     6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[[2,4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     3\n",
       "5    44\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[[1,5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first      13\n",
       "second    214\n",
       "third     210\n",
       "fourth    440\n",
       "fifth      53\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series([13,214,210,440,53],index=[\"first\",\"second\",\"third\",\"fourth\",\"fifth\"]) #We can also specify string indexes.\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first     13\n",
       "third    210\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[[\"first\",\"third\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Updating values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2\n",
       "1     3\n",
       "2    55\n",
       "3     2\n",
       "4     6\n",
       "5    44\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([2,3,55,2,6,44])\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1[0] =4 #We can assign new values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4\n",
       "1     3\n",
       "2    55\n",
       "3     2\n",
       "4     6\n",
       "5    44\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Pandas Daraframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A DataFrame is a table. It contains an array of individual entries, each of which has a certain value. Each entry corresponds to a row (or record) and a column.\n",
    "\n",
    "Those who are familiar with R know the data frame as a way to store data in rectangular grids that can easily be overviewed. Each row of these grids corresponds to measurements or values of an instance, while each column is a vector containing data for a specific variable. This means that a data frame’s rows do not need to contain, but can contain, the same type of values: they can be numeric, character, logical, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mellon</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Josh</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age\n",
       "0  Mellon   17\n",
       "1    Josh   34\n",
       "2    Mary   45"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'Name': [\"Mellon\",\"Josh\", \"Mary\"], 'Age': [17,34,45]}) #It can be created directly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can think keys as columns and values as rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mellon</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Josh</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age\n",
       "0  Mellon   17\n",
       "1    Josh   34\n",
       "2    Mary   45"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = {'Name': [\"Mellon\",\"Josh\", \"Mary\"], 'Age': [17,34,45]} #It can be created by external dic\n",
    "dataframe = pd.DataFrame(dic)\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    17\n",
       "1    34\n",
       "2    45\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe[\"Age\"] #We can access age column with this way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    17\n",
       "1    34\n",
       "2    45\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.Age #We can also access age column with this way but it gives error if there is same method as the same name as .(column_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   variable\n",
       "0        23\n",
       "1     35423\n",
       "2     25235\n",
       "3       325\n",
       "4       235\n",
       "5        75"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [23,35423,25235,325,235,75]\n",
    "df = pd.DataFrame(l,columns=[\"variable\"]) #We can create dataframes also with this way\n",
    "df # In general,dataframes are named as \"df\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anastacia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Names\n",
       "0       John\n",
       "1       Mike\n",
       "2      Julia\n",
       "3  Anastacia"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = [\"John\",\"Mike\",\"Julia\",\"Anastacia\"]\n",
    "df = pd.DataFrame(names,columns=[\"Names\"]) \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also create dataframes with numpy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  6,  7],\n",
       "       [ 3,  2,  1],\n",
       "       [ 2, 42,  5]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr = np.array([5,6,7,3,2,1,2,42,5]).reshape(3,3,)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable_1</th>\n",
       "      <th>Variable_2</th>\n",
       "      <th>Variable_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Variable_1  Variable_2  Variable_3\n",
       "0           5           6           7\n",
       "1           3           2           1\n",
       "2           2          42           5"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(arr,columns=[\"Variable_1\",\"Variable_2\",\"Variable_3\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attributes of a Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable_1</th>\n",
       "      <th>Variable_2</th>\n",
       "      <th>Variable_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Variable_1  Variable_2  Variable_3\n",
       "0           5           6           7\n",
       "1           3           2           1\n",
       "2           2          42           5"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(arr,columns=[\"Variable_1\",\"Variable_2\",\"Variable_3\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RangeIndex(start=0, stop=3, step=1),\n",
       " Index(['Variable_1', 'Variable_2', 'Variable_3'], dtype='object')]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.axes #It shows axes names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape #It shows shape of a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ndim #It shows dimension of a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size #It shows size of a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Variable_1', 'Variable_2', 'Variable_3'], dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=3, step=1)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  6,  7],\n",
       "       [ 3,  2,  1],\n",
       "       [ 2, 42,  5]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values #It shows values of a dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing Column Names in a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable_1</th>\n",
       "      <th>Variable_2</th>\n",
       "      <th>Variable_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Variable_1  Variable_2  Variable_3\n",
       "0           5           6           7\n",
       "1           3           2           1\n",
       "2           2          42           5"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Variable_1', 'Variable_2', 'Variable_3'], dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns #We can reach column of a dataframe with this way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = (\"Var1\",\"Var2\",\"Var3\") #We can change column names of a dataframe with this way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Var1</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Var3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Var1  Var2  Var3\n",
       "0     5     6     7\n",
       "1     3     2     1\n",
       "2     2    42     5"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing rows of a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Income  Age\n",
       "0     Josh    5000   35\n",
       "1     Mike    7000   19\n",
       "2      Ana    9000   26\n",
       "3  Yohanna    6000   32"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Ana\",\"Yohanna\"],\"Income\": [5000,7000,9000,6000],\n",
    "                   \"Age\":[35,19,26,32]})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to reach rows by their integer location, we can use **iloc** keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name      Josh\n",
       "Income    5000\n",
       "Age         35\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Income  Age\n",
       "0  Josh    5000   35\n",
       "2   Ana    9000   26"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fancy Indexing\n",
    "df.iloc[[0,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    35\n",
       "2    26\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also select spesific rows of a column\n",
    "df.iloc[[0,2],2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Income  Age\n",
       "0    5000   35\n",
       "2    9000   26"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[0,2],[1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Income  Age\n",
       "1  Mike    7000   19\n",
       "2   Ana    9000   26"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    19\n",
       "2    26\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3,2] #It gets just second column-age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to reach rows by their indexes(labels), we can use **loc** keyword. If indexes are like 0,1,2..., it can be same as using loc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Income  Age\n",
       "0     Josh    5000   35\n",
       "1     Mike    7000   19\n",
       "2      Ana    9000   26\n",
       "3  Yohanna    6000   32"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name      Josh\n",
       "Income    5000\n",
       "Age         35\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    35\n",
       "2    26\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[[0,2],\"Age\"] #We can reach spesific columns with name of column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Josh</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Mike</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Income  Age\n",
       "a     Josh    5000   35\n",
       "b     Mike    7000   19\n",
       "c      Ana    9000   26\n",
       "d  Yohanna    6000   32"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Ana\",\"Yohanna\"],\"Income\": [5000,7000,9000,6000],\n",
    "                   \"Age\":[35,19,26,32]},index=[\"a\",\"b\",\"c\",\"d\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name      Josh\n",
       "Income    5000\n",
       "Age         35\n",
       "Name: a, dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"a\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Josh</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Income  Age\n",
       "a     Josh    5000   35\n",
       "d  Yohanna    6000   32"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fancy indexing\n",
    "df.loc[[\"a\",\"d\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Mike</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Income  Age\n",
       "b     Mike    7000   19\n",
       "c      Ana    9000   26\n",
       "d  Yohanna    6000   32"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"b\":]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    9000\n",
       "d    6000\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"c\":,\"Income\"] #It gets just income column after index c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name      Josh\n",
       "Income    5000\n",
       "Age         35\n",
       "Name: a, dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing columns of a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Income  Age\n",
       "0     Josh    5000   35\n",
       "1     Mike    7000   19\n",
       "2      Ana    9000   26\n",
       "3  Yohanna    6000   32"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Ana\",\"Yohanna\"],\"Income\": [5000,7000,9000,6000],\n",
    "                   \"Age\":[35,19,26,32]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    35\n",
       "1    19\n",
       "2    26\n",
       "3    32\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age #We can access age column with this way but it gives error if there is same method as the same name as .(column_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    35\n",
       "1    19\n",
       "2    26\n",
       "3    32\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Age\"] #We can also access with this way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Age\"][0] #We can select spesific row of a column with this way"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assigning a value to spesific row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Income  Age\n",
       "0     Josh    5000   35\n",
       "1     Mike    7000   19\n",
       "2      Ana    9000   26\n",
       "3  Yohanna    6000   32"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Ana\",\"Yohanna\"],\"Income\": [5000,7000,9000,6000],\n",
    "                   \"Age\":[35,19,26,32]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[0] = [\"Berkay\",\"6000\",\"24\"] #We assign new values to a spesific row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Berkay</td>\n",
       "      <td>6000</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name Income Age\n",
       "0   Berkay   6000  24\n",
       "1     Mike   7000  19\n",
       "2      Ana   9000  26\n",
       "3  Yohanna   6000  32"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assigning Custom Indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Employee_Number</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>11286474</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>17588462</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ana</td>\n",
       "      <td>26735655</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>18653472</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Employee_Number  Income  Age\n",
       "0     Josh         11286474    5000   35\n",
       "1     Mike         17588462    7000   19\n",
       "2      Ana         26735655    9000   26\n",
       "3  Yohanna         18653472    6000   32"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Ana\",\"Yohanna\"],\"Employee_Number\": [11286474,17588462,26735655,18653472],\n",
    "                   \"Income\": [5000,7000,9000,6000],\"Age\":[35,19,26,32]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Employee_Number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11286474</th>\n",
       "      <td>Josh</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17588462</th>\n",
       "      <td>Mike</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26735655</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18653472</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name  Income  Age\n",
       "Employee_Number                      \n",
       "11286474            Josh    5000   35\n",
       "17588462            Mike    7000   19\n",
       "26735655             Ana    9000   26\n",
       "18653472         Yohanna    6000   32"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.set_index(\"Employee_Number\") #We assigned Employee number as index values\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name      Josh\n",
       "Income    5000\n",
       "Age         35\n",
       "Name: 11286474, dtype: object"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[11286474]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Employee_Number</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>11286474</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>17588462</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ana</td>\n",
       "      <td>26735655</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>18653472</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Employee_Number  Income  Age\n",
       "0     Josh         11286474    5000   35\n",
       "1     Mike         17588462    7000   19\n",
       "2      Ana         26735655    9000   26\n",
       "3  Yohanna         18653472    6000   32"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Ana\",\"Yohanna\"],\"Employee_Number\": [11286474,17588462,26735655,18653472],\n",
    "                   \"Income\": [5000,7000,9000,6000],\"Age\":[35,19,26,32]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Employee_Number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11286474</th>\n",
       "      <td>Josh</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17588462</th>\n",
       "      <td>Mike</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26735655</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18653472</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name  Income  Age\n",
       "Employee_Number                      \n",
       "11286474            Josh    5000   35\n",
       "17588462            Mike    7000   19\n",
       "26735655             Ana    9000   26\n",
       "18653472         Yohanna    6000   32"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(\"Employee_Number\",inplace=True) #Inplace argument assign new indexes directly\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resetting Indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Employee_Number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11286474</th>\n",
       "      <td>Josh</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17588462</th>\n",
       "      <td>Mike</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26735655</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18653472</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name  Income  Age\n",
       "Employee_Number                      \n",
       "11286474            Josh    5000   35\n",
       "17588462            Mike    7000   19\n",
       "26735655             Ana    9000   26\n",
       "18653472         Yohanna    6000   32"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Employee_Number</th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11286474</td>\n",
       "      <td>Josh</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17588462</td>\n",
       "      <td>Mike</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26735655</td>\n",
       "      <td>Ana</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18653472</td>\n",
       "      <td>Yohanna</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Employee_Number     Name  Income  Age\n",
       "0         11286474     Josh    5000   35\n",
       "1         17588462     Mike    7000   19\n",
       "2         26735655      Ana    9000   26\n",
       "3         18653472  Yohanna    6000   32"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting Indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Employee_Number</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Josh</td>\n",
       "      <td>11286474</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Mike</td>\n",
       "      <td>17588462</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Ana</td>\n",
       "      <td>26735655</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>18653472</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Employee_Number  Income  Age\n",
       "21     Josh         11286474    5000   35\n",
       "43     Mike         17588462    7000   19\n",
       "32      Ana         26735655    9000   26\n",
       "1   Yohanna         18653472    6000   32"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Ana\",\"Yohanna\"],\"Employee_Number\": [11286474,17588462,26735655,18653472],\n",
    "                   \"Income\": [5000,7000,9000,6000],\"Age\":[35,19,26,32]},index=[21,43,32,1])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Employee_Number</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yohanna</td>\n",
       "      <td>18653472</td>\n",
       "      <td>6000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Josh</td>\n",
       "      <td>11286474</td>\n",
       "      <td>5000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Ana</td>\n",
       "      <td>26735655</td>\n",
       "      <td>9000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Mike</td>\n",
       "      <td>17588462</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Employee_Number  Income  Age\n",
       "1   Yohanna         18653472    6000   32\n",
       "21     Josh         11286474    5000   35\n",
       "32      Ana         26735655    9000   26\n",
       "43     Mike         17588462    7000   19"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "0    Josh               IT    4800   24\n",
       "1    Mike  Human Resources    5200   28\n",
       "2   Julia          Finance    6600   33\n",
       "3  Sergio     Supply Chain    5700   41"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Julia\",\"Sergio\"],\n",
    "                          \"Department\":[\"IT\",\"Human Resources\",\"Finance\",\"Supply Chain\"],\n",
    "                          \"Income\":[4800,5200,6600,5700],\n",
    "                          \"Age\":[24,28,33,41]})\n",
    "employees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check if spesific values satisfy a condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "Name: Department, dtype: bool"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Department\"] == \"IT\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get values if condition satisfy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name Department  Income  Age\n",
       "0  Josh         IT    4800   24"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt = employees[\"Department\"] == \"IT\"\n",
    "employees[filt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name Department  Income  Age\n",
       "0  Josh         IT    4800   24"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[employees[\"Department\"] == \"IT\"] #or we can directly write it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtering with **loc** gives us flexibility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    5200\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.loc[employees[\"Department\"] == \"Human Resources\",\"Income\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering with aritmethic operators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "0    Josh               IT    4800   24\n",
       "1    Mike  Human Resources    5200   28\n",
       "2   Julia          Finance    6600   33\n",
       "3  Sergio     Supply Chain    5700   41"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Department  Income  Age\n",
       "2   Julia       Finance    6600   33\n",
       "3  Sergio  Supply Chain    5700   41"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[employees[\"Income\"]>5500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Department  Income  Age\n",
       "2   Julia       Finance    6600   33\n",
       "3  Sergio  Supply Chain    5700   41"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[employees[\"Age\"]>30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use **&** in order to try 2 condition. This is **and** logic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Department  Income  Age\n",
       "2   Julia       Finance    6600   33\n",
       "3  Sergio  Supply Chain    5700   41"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[(employees[\"Age\"]>30) & (employees[\"Income\"]>5000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Name, Department, Income, Age]\n",
       "Index: []"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[(employees[\"Name\"]==\"Sergio\") & (employees[\"Department\"]==\"IT\")] #There is no satisfying value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use **|** in order to try 2 condition. This is **or** logic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "1    Mike  Human Resources    5200   28\n",
       "2   Julia          Finance    6600   33\n",
       "3  Sergio     Supply Chain    5700   41"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[(employees[\"Age\"]>30) | (employees[\"Income\"]>5000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Department  Income  Age\n",
       "0    Josh            IT    4800   24\n",
       "3  Sergio  Supply Chain    5700   41"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[(employees[\"Name\"]==\"Sergio\") | (employees[\"Department\"]==\"IT\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Opposite Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get opposite of a filter with **~(Tilde)** sign."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "0    Josh               IT    4800   24\n",
       "1    Mike  Human Resources    5200   28\n",
       "2   Julia          Finance    6600   33\n",
       "3  Sergio     Supply Chain    5700   41"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name       Department  Income  Age\n",
       "0  Josh               IT    4800   24\n",
       "1  Mike  Human Resources    5200   28"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[~(employees[\"Income\"]>5300)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Department  Income  Age\n",
       "3  Sergio  Supply Chain    5700   41"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[~(employees[\"Age\"]<35)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering with filter() function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "0    Josh               IT    4800   24\n",
       "1    Mike  Human Resources    5200   28\n",
       "2   Julia          Finance    6600   33\n",
       "3  Sergio     Supply Chain    5700   41"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Department</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IT</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Human Resources</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Finance</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Department  Age\n",
       "0               IT   24\n",
       "1  Human Resources   28\n",
       "2          Finance   33\n",
       "3     Supply Chain   41"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.filter(items=[\"Department\",\"Age\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regex is another thema but we can also filter by using regex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Income  Age\n",
       "0    Josh    4800   24\n",
       "1    Mike    5200   28\n",
       "2   Julia    6600   33\n",
       "3  Sergio    5700   41"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.filter(regex='e$', axis=1) #Columns end with \"e\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding/Removing rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "0    Josh               IT    4800   24\n",
       "1    Mike  Human Resources    5200   28\n",
       "2   Julia          Finance    6600   33\n",
       "3  Sergio     Supply Chain    5700   41"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Julia\",\"Sergio\"],\n",
    "                          \"Department\":[\"IT\",\"Human Resources\",\"Finance\",\"Supply Chain\"],\n",
    "                          \"Income\":[4800,5200,6600,5700],\n",
    "                          \"Age\":[24,28,33,41]})\n",
    "employees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Rows - append()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berkay</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income   Age\n",
       "0    Josh               IT  4800.0  24.0\n",
       "1    Mike  Human Resources  5200.0  28.0\n",
       "2   Julia          Finance  6600.0  33.0\n",
       "3  Sergio     Supply Chain  5700.0  41.0\n",
       "4  Berkay              NaN     NaN   NaN"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.append({\"Name\": \"Berkay\"},ignore_index=True) #It adds automatically to the end of dataframe. But we need to add all values, otherwise it gives nan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berkay</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6000</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "0    Josh               IT    4800   24\n",
       "1    Mike  Human Resources    5200   28\n",
       "2   Julia          Finance    6600   33\n",
       "3  Sergio     Supply Chain    5700   41\n",
       "4  Berkay          Finance    6000   24"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.append({\"Name\":\"Berkay\",\"Department\":\"Finance\",\"Income\":6000,\"Age\":24},ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing Rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can remove rows by index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "0    Josh               IT    4800   24\n",
       "1    Mike  Human Resources    5200   28\n",
       "3  Sergio     Supply Chain    5700   41"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.drop(index=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can remove rows by conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Department  Income  Age\n",
       "2   Julia       Finance    6600   33\n",
       "3  Sergio  Supply Chain    5700   41"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[employees[\"Age\"]>30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name       Department  Income  Age\n",
       "0  Josh               IT    4800   24\n",
       "1  Mike  Human Resources    5200   28"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.drop(index=employees[employees[\"Age\"]>30].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "0    Josh               IT    4800   24\n",
       "1    Mike  Human Resources    5200   28\n",
       "2   Julia          Finance    6600   33\n",
       "3  Sergio     Supply Chain    5700   41"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "employees[\"Experience\"] = [5,6,2,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age  Experience\n",
       "0    Josh               IT    4800   24           5\n",
       "1    Mike  Human Resources    5200   28           6\n",
       "2   Julia          Finance    6600   33           2\n",
       "3  Sergio     Supply Chain    5700   41           8"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Age\n",
       "0    Josh               IT   24\n",
       "1    Mike  Human Resources   28\n",
       "2   Julia          Finance   33\n",
       "3  Sergio     Supply Chain   41"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.drop(columns=[\"Experience\",\"Income\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Age\n",
       "0    Josh               IT   24\n",
       "1    Mike  Human Resources   28\n",
       "2   Julia          Finance   33\n",
       "3  Sergio     Supply Chain   41"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.drop([\"Experience\",\"Income\"],axis=1) #Axis 1 means columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Age\n",
       "0    Josh               IT   24\n",
       "1    Mike  Human Resources   28\n",
       "2   Julia          Finance   33\n",
       "3  Sergio     Supply Chain   41"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fancy indexing gives same result\n",
    "employees[[\"Name\",\"Department\",\"Age\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging Dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concat() function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "0    Josh               IT    4800   24\n",
       "1    Mike  Human Resources    5200   28\n",
       "2   Julia          Finance    6600   33\n",
       "3  Sergio     Supply Chain    5700   41"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Julia\",\"Sergio\"],\n",
    "                          \"Department\":[\"IT\",\"Human Resources\",\"Finance\",\"Supply Chain\"],\n",
    "                          \"Income\":[4800,5200,6600,5700],\n",
    "                          \"Age\":[24,28,33,41]})\n",
    "employees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Berkay</td>\n",
       "      <td>Finance</td>\n",
       "      <td>5700</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>6900</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Christy</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>8700</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feder</td>\n",
       "      <td>Law</td>\n",
       "      <td>6300</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name       Department  Income  Age\n",
       "0   Berkay          Finance    5700   29\n",
       "1  Michael        Marketing    6900   33\n",
       "2  Christy  Human Resources    8700   29\n",
       "3    Feder              Law    6300   44"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees2 = pd.DataFrame({\"Name\":[\"Berkay\",\"Michael\",\"Christy\",\"Feder\"],\n",
    "                          \"Department\":[\"Finance\",\"Marketing\",\"Human Resources\",\"Law\"],\n",
    "                          \"Income\":[5700,6900,8700,6300],\n",
    "                          \"Age\":[29,33,29,44]})\n",
    "employees2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can merge dataframes with **concat()** function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Berkay</td>\n",
       "      <td>Finance</td>\n",
       "      <td>5700</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>6900</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Christy</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>8700</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feder</td>\n",
       "      <td>Law</td>\n",
       "      <td>6300</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name       Department  Income  Age\n",
       "0     Josh               IT    4800   24\n",
       "1     Mike  Human Resources    5200   28\n",
       "2    Julia          Finance    6600   33\n",
       "3   Sergio     Supply Chain    5700   41\n",
       "0   Berkay          Finance    5700   29\n",
       "1  Michael        Marketing    6900   33\n",
       "2  Christy  Human Resources    8700   29\n",
       "3    Feder              Law    6300   44"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([employees,employees2]) #It will merge without changing indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berkay</td>\n",
       "      <td>Finance</td>\n",
       "      <td>5700</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>6900</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Christy</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>8700</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Feder</td>\n",
       "      <td>Law</td>\n",
       "      <td>6300</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name       Department  Income  Age\n",
       "0     Josh               IT    4800   24\n",
       "1     Mike  Human Resources    5200   28\n",
       "2    Julia          Finance    6600   33\n",
       "3   Sergio     Supply Chain    5700   41\n",
       "4   Berkay          Finance    5700   29\n",
       "5  Michael        Marketing    6900   33\n",
       "6  Christy  Human Resources    8700   29\n",
       "7    Feder              Law    6300   44"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([employees,employees2],ignore_index=True) #It will update indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Joins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Join is a common concept coming from SQL. It helps us merging dataframes."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inner Join**\n",
    "\n",
    "An inner join requires each row in the two joined dataframes to have matching column values. This is similar to the intersection of two sets.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Student Number</th>\n",
       "      <th>GPA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mike</td>\n",
       "      <td>32212354</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9364859</td>\n",
       "      <td>2.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Janiel</td>\n",
       "      <td>847937250</td>\n",
       "      <td>3.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Student Number   GPA\n",
       "0    Mike        32212354  3.40\n",
       "1     Ana         9364859  2.78\n",
       "2  Janiel       847937250  3.01"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class1= pd.DataFrame({\"Name\":[\"Mike\",\"Ana\",\"Janiel\"],\"Student Number\":[32212354,9364859,847937250],\"GPA\":[3.40,2.78,3.01]})\n",
    "class1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Student Number</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kristina</td>\n",
       "      <td>738273948</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>422413342</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arhun</td>\n",
       "      <td>938674938</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Student Number  Age\n",
       "0  Kristina       738273948   21\n",
       "1   Michael       422413342   24\n",
       "2     Arhun       938674938   19"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class2= pd.DataFrame({\"Name\":[\"Kristina\",\"Michael\",\"Arhun\"],\"Student Number\":[738273948,422413342,938674938],\"Age\":[21,24,19]})\n",
    "class2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Student Number</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mike</td>\n",
       "      <td>32212354</td>\n",
       "      <td>3.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9364859</td>\n",
       "      <td>2.78</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Janiel</td>\n",
       "      <td>847937250</td>\n",
       "      <td>3.01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kristina</td>\n",
       "      <td>738273948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>422413342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arhun</td>\n",
       "      <td>938674938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Student Number   GPA   Age\n",
       "0      Mike        32212354  3.40   NaN\n",
       "1       Ana         9364859  2.78   NaN\n",
       "2    Janiel       847937250  3.01   NaN\n",
       "0  Kristina       738273948   NaN  21.0\n",
       "1   Michael       422413342   NaN  24.0\n",
       "2     Arhun       938674938   NaN  19.0"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([class1,class2]) #Every column is not sanme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Student Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mike</td>\n",
       "      <td>32212354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9364859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Janiel</td>\n",
       "      <td>847937250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kristina</td>\n",
       "      <td>738273948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>422413342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arhun</td>\n",
       "      <td>938674938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Student Number\n",
       "0      Mike        32212354\n",
       "1       Ana         9364859\n",
       "2    Janiel       847937250\n",
       "0  Kristina       738273948\n",
       "1   Michael       422413342\n",
       "2     Arhun       938674938"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([class1,class2],join=\"inner\") #Inner join just gets intersection of dataframes"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Full Join(Full Outer Join)**\n",
    "\n",
    "Full Join, also known as Full Outer Join, returns all those records which either have a match in the left or right dataframe.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Student Number</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mike</td>\n",
       "      <td>32212354</td>\n",
       "      <td>3.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9364859</td>\n",
       "      <td>2.78</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Janiel</td>\n",
       "      <td>847937250</td>\n",
       "      <td>3.01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kristina</td>\n",
       "      <td>738273948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>422413342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arhun</td>\n",
       "      <td>938674938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Student Number   GPA   Age\n",
       "0      Mike        32212354  3.40   NaN\n",
       "1       Ana         9364859  2.78   NaN\n",
       "2    Janiel       847937250  3.01   NaN\n",
       "0  Kristina       738273948   NaN  21.0\n",
       "1   Michael       422413342   NaN  24.0\n",
       "2     Arhun       938674938   NaN  19.0"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([class1,class2],join=\"outer\") #Full outer join just gets all of dataframes"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Left Join(Left Outer Join)**\n",
    "\n",
    "Left join, also known as Left Outer Join, returns a dataframe containing all the rows of the left dataframe. In order to do that, we use **merge()** function.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Student Number</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mike</td>\n",
       "      <td>32212354</td>\n",
       "      <td>3.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ana</td>\n",
       "      <td>9364859</td>\n",
       "      <td>2.78</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Janiel</td>\n",
       "      <td>847937250</td>\n",
       "      <td>3.01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Student Number   GPA  Age\n",
       "0    Mike        32212354  3.40  NaN\n",
       "1     Ana         9364859  2.78  NaN\n",
       "2  Janiel       847937250  3.01  NaN"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(class1,class2,how=\"left\") #Left join just gets all of rows of the left dataframe"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Right Join(Right Outer Join)**\n",
    "\n",
    "Right join, also known as Right Outer Join, is similar to the Left Outer Join. The only difference is that all the rows of the right dataframe are taken as it is and only those of the left dataframe that are common in both.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Student Number</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kristina</td>\n",
       "      <td>738273948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>422413342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arhun</td>\n",
       "      <td>938674938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Student Number  GPA  Age\n",
       "0  Kristina       738273948  NaN   21\n",
       "1   Michael       422413342  NaN   24\n",
       "2     Arhun       938674938  NaN   19"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(class1,class2,how=\"right\") #Right join just gets all of rows of the right dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also merge dataframes with many to many structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Student Number</th>\n",
       "      <th>Department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kristina</td>\n",
       "      <td>738273948</td>\n",
       "      <td>Informatics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>422413342</td>\n",
       "      <td>Computer Science</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arhun</td>\n",
       "      <td>938674938</td>\n",
       "      <td>Management</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Student Number        Department\n",
       "0  Kristina       738273948       Informatics\n",
       "1   Michael       422413342  Computer Science\n",
       "2     Arhun       938674938        Management"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class1= pd.DataFrame({\"Name\":[\"Kristina\",\"Michael\",\"Arhun\"],\"Student Number\":[738273948,422413342,938674938],\n",
    "                      \"Department\":[\"Informatics\",\"Computer Science\",\"Management\"]})\n",
    "class1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Department</th>\n",
       "      <th>Lectures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Informatics</td>\n",
       "      <td>MIS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Computer Science</td>\n",
       "      <td>Linear Algebra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Management</td>\n",
       "      <td>Introduction to Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Informatics</td>\n",
       "      <td>Data Mining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Computer Science</td>\n",
       "      <td>IT Project Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Management</td>\n",
       "      <td>Marketing 101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Department                 Lectures\n",
       "0       Informatics                      MIS\n",
       "1  Computer Science           Linear Algebra\n",
       "2        Management  Introduction to Finance\n",
       "3       Informatics              Data Mining\n",
       "4  Computer Science    IT Project Management\n",
       "5        Management            Marketing 101"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lectures = pd.DataFrame({\"Department\":[\"Informatics\",\"Computer Science\",\"Management\",\"Informatics\",\"Computer Science\",\"Management\"],\n",
    "                         \"Lectures\":[\"MIS\",\"Linear Algebra\",\"Introduction to Finance\",\"Data Mining\",\"IT Project Management\",\"Marketing 101\"]})\n",
    "lectures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Student Number</th>\n",
       "      <th>Department</th>\n",
       "      <th>Lectures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kristina</td>\n",
       "      <td>738273948</td>\n",
       "      <td>Informatics</td>\n",
       "      <td>MIS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kristina</td>\n",
       "      <td>738273948</td>\n",
       "      <td>Informatics</td>\n",
       "      <td>Data Mining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michael</td>\n",
       "      <td>422413342</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>Linear Algebra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Michael</td>\n",
       "      <td>422413342</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>IT Project Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arhun</td>\n",
       "      <td>938674938</td>\n",
       "      <td>Management</td>\n",
       "      <td>Introduction to Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Arhun</td>\n",
       "      <td>938674938</td>\n",
       "      <td>Management</td>\n",
       "      <td>Marketing 101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Student Number        Department                 Lectures\n",
       "0  Kristina       738273948       Informatics                      MIS\n",
       "1  Kristina       738273948       Informatics              Data Mining\n",
       "2   Michael       422413342  Computer Science           Linear Algebra\n",
       "3   Michael       422413342  Computer Science    IT Project Management\n",
       "4     Arhun       938674938        Management  Introduction to Finance\n",
       "5     Arhun       938674938        Management            Marketing 101"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(class1,lectures)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Append()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also merge dataframes with **append()** function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berkay</td>\n",
       "      <td>Finance</td>\n",
       "      <td>5700</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>6900</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Christy</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>8700</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Feder</td>\n",
       "      <td>Law</td>\n",
       "      <td>6300</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name       Department  Income  Age\n",
       "0     Josh               IT    4800   24\n",
       "1     Mike  Human Resources    5200   28\n",
       "2    Julia          Finance    6600   33\n",
       "3   Sergio     Supply Chain    5700   41\n",
       "4   Berkay          Finance    5700   29\n",
       "5  Michael        Marketing    6900   33\n",
       "6  Christy  Human Resources    8700   29\n",
       "7    Feder              Law    6300   44"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.append(employees2,ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>7200</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "0    Josh               IT    4800   24\n",
       "1    Mike  Human Resources    5200   28\n",
       "2   Julia          Finance    6600   33\n",
       "3  Sergio     Supply Chain    5700   41\n",
       "4   Julia          Finance    7200   22"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Julia\",\"Sergio\",\"Julia\"],\n",
    "                          \"Department\":[\"IT\",\"Human Resources\",\"Finance\",\"Supply Chain\",\"Finance\"],\n",
    "                          \"Income\":[4800,5200,6600,5700,7200],\n",
    "                          \"Age\":[24,28,33,41,22]})\n",
    "employees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>7200</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "4   Julia          Finance    7200   22\n",
       "0    Josh               IT    4800   24\n",
       "1    Mike  Human Resources    5200   28\n",
       "2   Julia          Finance    6600   33\n",
       "3  Sergio     Supply Chain    5700   41"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.sort_values(by=\"Age\") #We sort it by age column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>7200</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "3  Sergio     Supply Chain    5700   41\n",
       "2   Julia          Finance    6600   33\n",
       "1    Mike  Human Resources    5200   28\n",
       "0    Josh               IT    4800   24\n",
       "4   Julia          Finance    7200   22"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.sort_values(by=\"Age\",ascending=False) #We sort it by descending age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>7200</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "0    Josh               IT    4800   24\n",
       "2   Julia          Finance    6600   33\n",
       "4   Julia          Finance    7200   22\n",
       "1    Mike  Human Resources    5200   28\n",
       "3  Sergio     Supply Chain    5700   41"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.sort_values(by=[\"Name\",\"Income\"]) #It will first sort by name and then it will compare income column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>7200</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department  Income  Age\n",
       "0    Josh               IT    4800   24\n",
       "2   Julia          Finance    6600   33\n",
       "4   Julia          Finance    7200   22\n",
       "1    Mike  Human Resources    5200   28\n",
       "3  Sergio     Supply Chain    5700   41"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.sort_values(by=[\"Name\",\"Age\"],ascending=[True,False])\n",
    "#We can also sort one column ascending order and descending with other column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to get largest elements in a column, we can use **nlargest()** function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    7200\n",
       "2    6600\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Income\"].nlargest(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to get smallest elements in a column, we can use **nsmallest()** function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4800\n",
       "1    5200\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Income\"].nsmallest(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregation Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas has a number of aggregating functions that reduce the dimension of the grouped object.\n",
    "\n",
    "**Common Aggregation Functions**\n",
    "\n",
    "- count()\n",
    "- value_count()\n",
    "- mean()\n",
    "- median()\n",
    "- sum()\n",
    "- min()\n",
    "- max()\n",
    "- std()\n",
    "- var()\n",
    "- describe()\n",
    "- sem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>7200</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>8400</td>\n",
       "      <td>46</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>IT</td>\n",
       "      <td>7700</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jakub</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Law</td>\n",
       "      <td>9400</td>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name       Department  Income  Age  Experience\n",
       "0     Josh               IT    4800   24           2\n",
       "1     Mike  Human Resources    5200   28           5\n",
       "2    Julia          Finance    6600   33           9\n",
       "3   Sergio     Supply Chain    5700   41          17\n",
       "4    Julia          Finance    7200   22           1\n",
       "5  Michael        Marketing    8400   46          24\n",
       "6   Sarath               IT    7700   31          10\n",
       "7    Jakub  Human Resources    4200   27           6\n",
       "8    Chris              Law    9400   39          13"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Julia\",\"Sergio\",\"Julia\",\"Michael\",\"Sarath\",\"Jakub\",\"Chris\"],\n",
    "                          \"Department\":[\"IT\",\"Human Resources\",\"Finance\",\"Supply Chain\",\"Finance\",\"Marketing\",\"IT\",\"Human Resources\",\"Law\"],\n",
    "                          \"Income\":[4800,5200,6600,5700,7200,8400,7700,4200,9400],\n",
    "                          \"Age\":[24,28,33,41,22,46,31,27,39],\n",
    "                          \"Experience\":[2,5,9,17,1,24,10,6,13]})\n",
    "employees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name          9\n",
       "Department    9\n",
       "Income        9\n",
       "Age           9\n",
       "Experience    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.count() #It count elements by columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Human Resources    2\n",
       "Finance            2\n",
       "IT                 2\n",
       "Supply Chain       1\n",
       "Marketing          1\n",
       "Law                1\n",
       "Name: Department, dtype: int64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Department\"].value_counts() #It count values in a column. It just work with series!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Income        6577.777778\n",
       "Age             32.333333\n",
       "Experience       9.666667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.mean() #Compute mean of each column if it's numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6577.777777777777"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Income\"].mean() # We can also compute mean of a spesific column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Income        6600.0\n",
       "Age             31.0\n",
       "Experience       9.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.median() #Compute median of each column if it's numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6600.0"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Income\"].median() # We can also compute median of a spesific column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name            JoshMikeJuliaSergioJuliaMichaelSarathJakubChris\n",
       "Department    ITHuman ResourcesFinanceSupply ChainFinanceMar...\n",
       "Income                                                    59200\n",
       "Age                                                         291\n",
       "Experience                                                   87\n",
       "dtype: object"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.sum() #Compute sum of each column if it's numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59200"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Income\"].sum() # We can also compute sum of a spesific column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name            Chris\n",
       "Department    Finance\n",
       "Income           4200\n",
       "Age                22\n",
       "Experience          1\n",
       "dtype: object"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.min() #Compute mminimum value of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Age\"].min() # We can also compute minimum value of a spesific column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                Sergio\n",
       "Department    Supply Chain\n",
       "Income                9400\n",
       "Age                     46\n",
       "Experience              24\n",
       "dtype: object"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.max() #Compute maximum value of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Age\"].max() # We can also compute maximum value of a spesific column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Income        1746.981524\n",
       "Age              8.154753\n",
       "Experience       7.416198\n",
       "dtype: float64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.std() #Compute standart deviation of each column if it's numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.154753215150045"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Age\"].std() # We can also compute standart deviation of a spesific column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Income        3.051944e+06\n",
       "Age           6.650000e+01\n",
       "Experience    5.500000e+01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.var() #Compute variance of each column if it's numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66.5"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Age\"].var() # We can also compute variance of a spesific column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6577.777778</td>\n",
       "      <td>32.333333</td>\n",
       "      <td>9.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1746.981524</td>\n",
       "      <td>8.154753</td>\n",
       "      <td>7.416198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4200.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5200.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6600.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7700.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9400.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Income        Age  Experience\n",
       "count     9.000000   9.000000    9.000000\n",
       "mean   6577.777778  32.333333    9.666667\n",
       "std    1746.981524   8.154753    7.416198\n",
       "min    4200.000000  22.000000    1.000000\n",
       "25%    5200.000000  27.000000    5.000000\n",
       "50%    6600.000000  31.000000    9.000000\n",
       "75%    7700.000000  39.000000   13.000000\n",
       "max    9400.000000  46.000000   24.000000"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.describe() #It computes a quick summary of values per group if it's numeric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>9.0</td>\n",
       "      <td>6577.777778</td>\n",
       "      <td>1746.981524</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>7700.0</td>\n",
       "      <td>9400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>9.0</td>\n",
       "      <td>32.333333</td>\n",
       "      <td>8.154753</td>\n",
       "      <td>22.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Experience</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.666667</td>\n",
       "      <td>7.416198</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count         mean          std     min     25%     50%     75%  \\\n",
       "Income        9.0  6577.777778  1746.981524  4200.0  5200.0  6600.0  7700.0   \n",
       "Age           9.0    32.333333     8.154753    22.0    27.0    31.0    39.0   \n",
       "Experience    9.0     9.666667     7.416198     1.0     5.0     9.0    13.0   \n",
       "\n",
       "               max  \n",
       "Income      9400.0  \n",
       "Age           46.0  \n",
       "Experience    24.0  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.describe().T #We can also see transpoze of it to see better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Income        582.327175\n",
       "Age             2.718251\n",
       "Experience      2.472066\n",
       "dtype: float64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.sem() #It computes standard error of the mean values for each column if it's numeric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any groupby operation involves one of the following operations on the original object. They are;\n",
    "\n",
    "- Splitting the Object\n",
    "\n",
    "- Applying a function\n",
    "\n",
    "- Combining the results\n",
    "\n",
    "Value which is used for grouping must be **categorical variable**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>7200</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>8400</td>\n",
       "      <td>46</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>IT</td>\n",
       "      <td>7700</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jakub</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Law</td>\n",
       "      <td>9400</td>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name       Department  Income  Age  Experience\n",
       "0     Josh               IT    4800   24           2\n",
       "1     Mike  Human Resources    5200   28           5\n",
       "2    Julia          Finance    6600   33           9\n",
       "3   Sergio     Supply Chain    5700   41          17\n",
       "4    Julia          Finance    7200   22           1\n",
       "5  Michael        Marketing    8400   46          24\n",
       "6   Sarath               IT    7700   31          10\n",
       "7    Jakub  Human Resources    4200   27           6\n",
       "8    Chris              Law    9400   39          13"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Julia\",\"Sergio\",\"Julia\",\"Michael\",\"Sarath\",\"Jakub\",\"Chris\"],\n",
    "                          \"Department\":[\"IT\",\"Human Resources\",\"Finance\",\"Supply Chain\",\"Finance\",\"Marketing\",\"IT\",\"Human Resources\",\"Law\"],\n",
    "                          \"Income\":[4800,5200,6600,5700,7200,8400,7700,4200,9400],\n",
    "                          \"Age\":[24,28,33,41,22,46,31,27,39],\n",
    "                          \"Experience\":[2,5,9,17,1,24,10,6,13]})\n",
    "employees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Departments are categorical variables and we can to group employees by their department. In order to do that, we use **groupby()** function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x11970feb0>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby(\"Department\") #It will create an object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>IT</td>\n",
       "      <td>7700</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name Department  Income  Age  Experience\n",
       "0    Josh         IT    4800   24           2\n",
       "6  Sarath         IT    7700   31          10"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees_departments = employees.groupby(\"Department\")\n",
    "employees_departments.get_group(\"IT\") #We can get the rows that employee is working in IT department."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also do that with filtering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>IT</td>\n",
       "      <td>7700</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name Department  Income  Age  Experience\n",
       "0    Josh         IT    4800   24           2\n",
       "6  Sarath         IT    7700   31          10"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[employees[\"Department\"]==\"IT\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can apply a function to the group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>6900.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>4700.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>6250.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Law</th>\n",
       "      <td>9400.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing</th>\n",
       "      <td>8400.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supply Chain</th>\n",
       "      <td>5700.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Income   Age  Experience\n",
       "Department                               \n",
       "Finance          6900.0  27.5         5.0\n",
       "Human Resources  4700.0  27.5         5.5\n",
       "IT               6250.0  27.5         6.0\n",
       "Law              9400.0  39.0        13.0\n",
       "Marketing        8400.0  46.0        24.0\n",
       "Supply Chain     5700.0  41.0        17.0"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby(\"Department\").mean() #We can get mean of every department."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Department\n",
       "Finance            6900.0\n",
       "Human Resources    4700.0\n",
       "IT                 6250.0\n",
       "Law                9400.0\n",
       "Marketing          8400.0\n",
       "Supply Chain       5700.0\n",
       "Name: Income, dtype: float64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby(\"Department\").mean()[\"Income\"] #We can also get mean of each department by a spesific column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Department\n",
       "Finance            10\n",
       "Human Resources    11\n",
       "IT                 12\n",
       "Law                13\n",
       "Marketing          24\n",
       "Supply Chain       17\n",
       "Name: Experience, dtype: int64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby(\"Department\")[\"Experience\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Department\n",
       "Finance            27.5\n",
       "Human Resources    27.5\n",
       "IT                 27.5\n",
       "Law                39.0\n",
       "Marketing          46.0\n",
       "Supply Chain       41.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby(\"Department\").mean()[\"Age\"] #Mean of age by each department"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>2.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>7.778175</td>\n",
       "      <td>22.0</td>\n",
       "      <td>24.75</td>\n",
       "      <td>27.5</td>\n",
       "      <td>30.25</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>2.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.25</td>\n",
       "      <td>27.5</td>\n",
       "      <td>27.75</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>2.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>4.949747</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.75</td>\n",
       "      <td>27.5</td>\n",
       "      <td>29.25</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Law</th>\n",
       "      <td>1.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing</th>\n",
       "      <td>1.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.00</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.00</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supply Chain</th>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count  mean       std   min    25%   50%    75%   max\n",
       "Department                                                            \n",
       "Finance            2.0  27.5  7.778175  22.0  24.75  27.5  30.25  33.0\n",
       "Human Resources    2.0  27.5  0.707107  27.0  27.25  27.5  27.75  28.0\n",
       "IT                 2.0  27.5  4.949747  24.0  25.75  27.5  29.25  31.0\n",
       "Law                1.0  39.0       NaN  39.0  39.00  39.0  39.00  39.0\n",
       "Marketing          1.0  46.0       NaN  46.0  46.00  46.0  46.00  46.0\n",
       "Supply Chain       1.0  41.0       NaN  41.0  41.00  41.0  41.00  41.0"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby(\"Department\")[\"Age\"].describe() #We also can see with describe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to apply more than 1 function, we need to use **agg()**. It is same as **aggregate()**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>6900</td>\n",
       "      <td>7200</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>4700</td>\n",
       "      <td>5200</td>\n",
       "      <td>4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>6250</td>\n",
       "      <td>7700</td>\n",
       "      <td>6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Law</th>\n",
       "      <td>9400</td>\n",
       "      <td>9400</td>\n",
       "      <td>9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing</th>\n",
       "      <td>8400</td>\n",
       "      <td>8400</td>\n",
       "      <td>8400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supply Chain</th>\n",
       "      <td>5700</td>\n",
       "      <td>5700</td>\n",
       "      <td>5700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean   max  median\n",
       "Department                         \n",
       "Finance          6900  7200    6900\n",
       "Human Resources  4700  5200    4700\n",
       "IT               6250  7700    6250\n",
       "Law              9400  9400    9400\n",
       "Marketing        8400  8400    8400\n",
       "Supply Chain     5700  5700    5700"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby(\"Department\")[\"Income\"].agg([\"mean\",\"max\",\"median\"]) \n",
    "#We get mean, max and median of Income in each department."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean      6900\n",
       "max       7200\n",
       "median    6900\n",
       "Name: Finance, dtype: int64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby(\"Department\")[\"Income\"].agg([\"mean\",\"max\",\"median\"]).loc[\"Finance\"] \n",
    "#We get mean, max and median of Income in Finance department."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Name</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Income</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Age</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Experience</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Julia</td>\n",
       "      <td>7200</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Jakub</td>\n",
       "      <td>5200</td>\n",
       "      <td>4200</td>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>Josh</td>\n",
       "      <td>7700</td>\n",
       "      <td>4800</td>\n",
       "      <td>31</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Law</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Chris</td>\n",
       "      <td>9400</td>\n",
       "      <td>9400</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Michael</td>\n",
       "      <td>8400</td>\n",
       "      <td>8400</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supply Chain</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Sergio</td>\n",
       "      <td>5700</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name          Income       Age     Experience    \n",
       "                     max      min    max   min max min        max min\n",
       "Department                                                           \n",
       "Finance            Julia    Julia   7200  6600  33  22          9   1\n",
       "Human Resources     Mike    Jakub   5200  4200  28  27          6   5\n",
       "IT                Sarath     Josh   7700  4800  31  24         10   2\n",
       "Law                Chris    Chris   9400  9400  39  39         13  13\n",
       "Marketing        Michael  Michael   8400  8400  46  46         24  24\n",
       "Supply Chain      Sergio   Sergio   5700  5700  41  41         17  17"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby(\"Department\").aggregate([\"max\",\"min\"])\n",
    "#We get max and min values in each department."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>6900</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>4700</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>6250</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Law</th>\n",
       "      <td>9400</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing</th>\n",
       "      <td>8400</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supply Chain</th>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Income  Age\n",
       "Department                  \n",
       "Finance            6900   33\n",
       "Human Resources    4700   28\n",
       "IT                 6250   31\n",
       "Law                9400   39\n",
       "Marketing          8400   46\n",
       "Supply Chain       5700   41"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby(\"Department\").aggregate({\"Income\": \"mean\",\"Age\":\"max\"})\n",
    "#We can get different statistics for different columns thanks to dictionary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**transform() operation**\n",
    "\n",
    "Pandas DataFrame.transform() function call func on self producing a DataFrame with transformed values and that has the same axis length as self.\n",
    "\n",
    "The first argument **func** is to specify the function to be used for manipulating data. It can be a function, a string function name, a list of functions, or a dictionary of axis label.\n",
    "\n",
    "The second argument axis is to specify which axis the func is applied to. 0 for applying the func to each column and 1 for applying the func to each row."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to make a 1000$ salary increase to each employee. First, we define a function that increase the salaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "def salary_increase(x):\n",
    "    return x+1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4800\n",
       "1    5200\n",
       "2    6600\n",
       "3    5700\n",
       "4    7200\n",
       "5    8400\n",
       "6    7700\n",
       "7    4200\n",
       "8    9400\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Income\"] #Current salaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5800\n",
       "1     6200\n",
       "2     7600\n",
       "3     6700\n",
       "4     8200\n",
       "5     9400\n",
       "6     8700\n",
       "7     5200\n",
       "8    10400\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Income\"].transform(salary_increase) # It increase every salary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use **lambda** for shorter way. It's a functional programming logic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5800\n",
       "1     6200\n",
       "2     7600\n",
       "3     6700\n",
       "4     8200\n",
       "5     9400\n",
       "6     8700\n",
       "7     5200\n",
       "8    10400\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Income\"].transform(lambda x: x+1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -1.017628\n",
       "1   -0.788662\n",
       "2    0.012720\n",
       "3   -0.502454\n",
       "4    0.356170\n",
       "5    1.043069\n",
       "6    0.642378\n",
       "7   -1.361078\n",
       "8    1.615485\n",
       "Name: Income, dtype: float64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Income\"].transform(lambda x: (x-x.mean())/x.std()) #Standartizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69.282032</td>\n",
       "      <td>4.898979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72.111026</td>\n",
       "      <td>5.291503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81.240384</td>\n",
       "      <td>5.744563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75.498344</td>\n",
       "      <td>6.403124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84.852814</td>\n",
       "      <td>4.690416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>91.651514</td>\n",
       "      <td>6.782330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>87.749644</td>\n",
       "      <td>5.567764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64.807407</td>\n",
       "      <td>5.196152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>96.953597</td>\n",
       "      <td>6.244998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Income       Age\n",
       "0  69.282032  4.898979\n",
       "1  72.111026  5.291503\n",
       "2  81.240384  5.744563\n",
       "3  75.498344  6.403124\n",
       "4  84.852814  4.690416\n",
       "5  91.651514  6.782330\n",
       "6  87.749644  5.567764\n",
       "7  64.807407  5.196152\n",
       "8  96.953597  6.244998"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[[\"Income\",\"Age\"]].transform(\"sqrt\")#We can also apply a string function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It applies a spesific function all values of a dataframe of serie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>7200</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>8400</td>\n",
       "      <td>46</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>IT</td>\n",
       "      <td>7700</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jakub</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Law</td>\n",
       "      <td>9400</td>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name       Department  Income  Age  Experience\n",
       "0     Josh               IT    4800   24           2\n",
       "1     Mike  Human Resources    5200   28           5\n",
       "2    Julia          Finance    6600   33           9\n",
       "3   Sergio     Supply Chain    5700   41          17\n",
       "4    Julia          Finance    7200   22           1\n",
       "5  Michael        Marketing    8400   46          24\n",
       "6   Sarath               IT    7700   31          10\n",
       "7    Jakub  Human Resources    4200   27           6\n",
       "8    Chris              Law    9400   39          13"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Julia\",\"Sergio\",\"Julia\",\"Michael\",\"Sarath\",\"Jakub\",\"Chris\"],\n",
    "                          \"Department\":[\"IT\",\"Human Resources\",\"Finance\",\"Supply Chain\",\"Finance\",\"Marketing\",\"IT\",\"Human Resources\",\"Law\"],\n",
    "                          \"Income\":[4800,5200,6600,5700,7200,8400,7700,4200,9400],\n",
    "                          \"Age\":[24,28,33,41,22,46,31,27,39],\n",
    "                          \"Experience\":[2,5,9,17,1,24,10,6,13]})\n",
    "employees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    4\n",
       "2    5\n",
       "3    6\n",
       "4    5\n",
       "5    7\n",
       "6    6\n",
       "7    5\n",
       "8    5\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Name\"].apply(len) #We get length of all names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "def increase_age(x):\n",
    "    return x+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    25\n",
       "1    29\n",
       "2    34\n",
       "3    42\n",
       "4    23\n",
       "5    47\n",
       "6    32\n",
       "7    28\n",
       "8    40\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Age\"].apply(increase_age) #We increase each age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    25\n",
       "1    29\n",
       "2    34\n",
       "3    42\n",
       "4    23\n",
       "5    47\n",
       "6    32\n",
       "7    28\n",
       "8    40\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Age\"].apply(lambda x: x+1) #We increase each age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name            JoshMikeJuliaSergioJuliaMichaelSarathJakubChris\n",
       "Department    ITHuman ResourcesFinanceSupply ChainFinanceMar...\n",
       "Income                                                    59200\n",
       "Age                                                         291\n",
       "Experience                                                   87\n",
       "dtype: object"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.apply(np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Department\n",
       "Finance            13800\n",
       "Human Resources     9400\n",
       "IT                 12500\n",
       "Law                 9400\n",
       "Marketing           8400\n",
       "Supply Chain        5700\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby(\"Department\")[\"Income\"].apply(np.sum) #We get sum of salaries by each department."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Department\n",
       "Finance            22\n",
       "Human Resources    27\n",
       "IT                 24\n",
       "Law                39\n",
       "Marketing          46\n",
       "Supply Chain       41\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby(\"Department\")[\"Age\"].apply(min) #We get smallest age by each department."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9400</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4200</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Income  Age\n",
       "max    9400   46\n",
       "min    4200   22"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.loc[:,[\"Income\",\"Age\"]].apply([\"max\",\"min\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use **applymap()** function in order to apply a function to a Dataframe elementwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>7200</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>8400</td>\n",
       "      <td>46</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>IT</td>\n",
       "      <td>7700</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jakub</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Law</td>\n",
       "      <td>9400</td>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name       Department  Income  Age  Experience\n",
       "0     Josh               IT    4800   24           2\n",
       "1     Mike  Human Resources    5200   28           5\n",
       "2    Julia          Finance    6600   33           9\n",
       "3   Sergio     Supply Chain    5700   41          17\n",
       "4    Julia          Finance    7200   22           1\n",
       "5  Michael        Marketing    8400   46          24\n",
       "6   Sarath               IT    7700   31          10\n",
       "7    Jakub  Human Resources    4200   27           6\n",
       "8    Chris              Law    9400   39          13"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Department\n",
       "0     4           2\n",
       "1     4          15\n",
       "2     5           7\n",
       "3     6          12\n",
       "4     5           7\n",
       "5     7           9\n",
       "6     6           2\n",
       "7     5          15\n",
       "8     5           3"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[[\"Name\",\"Department\"]].applymap(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JOSH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MIKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JULIA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SERGIO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JULIA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MICHAEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SARATH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>JAKUB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CHRIS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name\n",
       "0     JOSH\n",
       "1     MIKE\n",
       "2    JULIA\n",
       "3   SERGIO\n",
       "4    JULIA\n",
       "5  MICHAEL\n",
       "6   SARATH\n",
       "7    JAKUB\n",
       "8    CHRIS"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[[\"Name\"]].applymap(str.upper) #We get upper values of each name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use **map()** function in order to map values of Series according to input correspondence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Information Technology\n",
       "1                       NaN\n",
       "2                       NaN\n",
       "3                       NaN\n",
       "4                       NaN\n",
       "5                       NaN\n",
       "6    Information Technology\n",
       "7                       NaN\n",
       "8                       NaN\n",
       "Name: Department, dtype: object"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Department\"].map({\"IT\":\"Information Technology\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But it also looks other values. In order to apply that just to IT, we will use **replace()** method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "employees[\"Department\"].replace({\"IT\":\"Information Technology\"},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>7200</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>8400</td>\n",
       "      <td>46</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>7700</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jakub</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Law</td>\n",
       "      <td>9400</td>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name              Department  Income  Age  Experience\n",
       "0     Josh  Information Technology    4800   24           2\n",
       "1     Mike         Human Resources    5200   28           5\n",
       "2    Julia                 Finance    6600   33           9\n",
       "3   Sergio            Supply Chain    5700   41          17\n",
       "4    Julia                 Finance    7200   22           1\n",
       "5  Michael               Marketing    8400   46          24\n",
       "6   Sarath  Information Technology    7700   31          10\n",
       "7    Jakub         Human Resources    4200   27           6\n",
       "8    Chris                     Law    9400   39          13"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot Tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It creates a spreadsheet-style pivot table as a DataFrame.\n",
    "\n",
    "The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Work Level</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>WL3</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>WL2</td>\n",
       "      <td>5200</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>WL2</td>\n",
       "      <td>6600</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>WL1</td>\n",
       "      <td>5700</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>WL3</td>\n",
       "      <td>7200</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department Work Level  Income  Age  Experience\n",
       "0    Josh               IT        WL3    4800   24           2\n",
       "1    Mike  Human Resources        WL2    5200   28           5\n",
       "2   Julia          Finance        WL2    6600   33           9\n",
       "3  Sergio     Supply Chain        WL1    5700   41          17\n",
       "4   Julia          Finance        WL3    7200   22           1"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees = pd.DataFrame({\"Name\":[\"Josh\",\"Mike\",\"Julia\",\"Sergio\",\"Julia\",\"Michael\",\"Sarath\",\"Jakub\",\"Chris\"],\n",
    "                          \"Department\":[\"IT\",\"Human Resources\",\"Finance\",\"Supply Chain\",\"Finance\",\"Marketing\",\"IT\",\"Human Resources\",\"Law\"],\n",
    "                          \"Work Level\":[\"WL3\",\"WL2\",\"WL2\",\"WL1\",\"WL3\",\"WL2\",\"WL1\",\"WL3\",\"WL1\"],\n",
    "                          \"Income\":[4800,5200,6600,5700,7200,8400,7700,4200,9400],\n",
    "                          \"Age\":[24,28,33,41,22,46,31,27,39],\n",
    "                          \"Experience\":[2,5,9,17,1,24,10,6,13]})\n",
    "employees.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Department\n",
       "Finance            6900\n",
       "Human Resources    4700\n",
       "IT                 6250\n",
       "Law                9400\n",
       "Marketing          8400\n",
       "Supply Chain       5700\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby(\"Department\")[\"Income\"].mean() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th>Work Level</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Finance</th>\n",
       "      <th>WL2</th>\n",
       "      <td>6600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WL3</th>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Human Resources</th>\n",
       "      <th>WL2</th>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WL3</th>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IT</th>\n",
       "      <th>WL1</th>\n",
       "      <td>7700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WL3</th>\n",
       "      <td>4800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Law</th>\n",
       "      <th>WL1</th>\n",
       "      <td>9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing</th>\n",
       "      <th>WL2</th>\n",
       "      <td>8400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supply Chain</th>\n",
       "      <th>WL1</th>\n",
       "      <td>5700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Income\n",
       "Department      Work Level        \n",
       "Finance         WL2           6600\n",
       "                WL3           7200\n",
       "Human Resources WL2           5200\n",
       "                WL3           4200\n",
       "IT              WL1           7700\n",
       "                WL3           4800\n",
       "Law             WL1           9400\n",
       "Marketing       WL2           8400\n",
       "Supply Chain    WL1           5700"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby([\"Department\",\"Work Level\"])[[\"Income\"]].agg(\"mean\")\n",
    "#We get mean of income according to department and work level. We see no nan values here, because it does not get na values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Work Level</th>\n",
       "      <th>WL1</th>\n",
       "      <th>WL2</th>\n",
       "      <th>WL3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>7200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>4200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>7700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Law</th>\n",
       "      <td>9400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supply Chain</th>\n",
       "      <td>5700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Income                \n",
       "Work Level          WL1     WL2     WL3\n",
       "Department                             \n",
       "Finance             NaN  6600.0  7200.0\n",
       "Human Resources     NaN  5200.0  4200.0\n",
       "IT               7700.0     NaN  4800.0\n",
       "Law              9400.0     NaN     NaN\n",
       "Marketing           NaN  8400.0     NaN\n",
       "Supply Chain     5700.0     NaN     NaN"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.groupby([\"Department\",\"Work Level\"])[[\"Income\"]].agg(\"mean\").unstack()\n",
    "#We get mean of income according to department and work level. Unstack() helps to see better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Work Level</th>\n",
       "      <th>WL1</th>\n",
       "      <th>WL2</th>\n",
       "      <th>WL3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>7200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>4200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>7700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Law</th>\n",
       "      <td>9400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supply Chain</th>\n",
       "      <td>5700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Work Level          WL1     WL2     WL3\n",
       "Department                             \n",
       "Finance             NaN  6600.0  7200.0\n",
       "Human Resources     NaN  5200.0  4200.0\n",
       "IT               7700.0     NaN  4800.0\n",
       "Law              9400.0     NaN     NaN\n",
       "Marketing           NaN  8400.0     NaN\n",
       "Supply Chain     5700.0     NaN     NaN"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.pivot_table(\"Income\",index=\"Department\",columns=\"Work Level\") #We can make it easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (0, 9]\n",
       "1      (0, 9]\n",
       "2      (0, 9]\n",
       "3    (16, 24]\n",
       "4      (0, 9]\n",
       "5    (16, 24]\n",
       "6     (9, 16]\n",
       "7      (0, 9]\n",
       "8     (9, 16]\n",
       "Name: Experience, dtype: category\n",
       "Categories (3, interval[int64]): [(0, 9] < (9, 16] < (16, 24]]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp = pd.cut(employees[\"Experience\"],[0,9,16,24])\n",
    "exp #We will create a new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Work Level</th>\n",
       "      <th>WL1</th>\n",
       "      <th>WL2</th>\n",
       "      <th>WL3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th>Experience</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <th>(0, 9]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>7200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <th>(0, 9]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>4200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IT</th>\n",
       "      <th>(0, 9]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(9, 16]</th>\n",
       "      <td>7700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Law</th>\n",
       "      <th>(9, 16]</th>\n",
       "      <td>9400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing</th>\n",
       "      <th>(16, 24]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supply Chain</th>\n",
       "      <th>(16, 24]</th>\n",
       "      <td>5700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Work Level                     WL1     WL2     WL3\n",
       "Department      Experience                        \n",
       "Finance         (0, 9]         NaN  6600.0  7200.0\n",
       "Human Resources (0, 9]         NaN  5200.0  4200.0\n",
       "IT              (0, 9]         NaN     NaN  4800.0\n",
       "                (9, 16]     7700.0     NaN     NaN\n",
       "Law             (9, 16]     9400.0     NaN     NaN\n",
       "Marketing       (16, 24]       NaN  8400.0     NaN\n",
       "Supply Chain    (16, 24]    5700.0     NaN     NaN"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.pivot_table(\"Income\",index=[\"Department\",exp],columns=\"Work Level\") #We can make it easier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing values(NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Work Level</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>WL3</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>WL2</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>WL2</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>WL1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>None</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name       Department Work Level  Income   Age  Experience\n",
       "0    Josh               IT        WL3  4800.0  24.0         2.0\n",
       "1    None  Human Resources        WL2  5200.0  28.0         NaN\n",
       "2   Julia          Finance        WL2  6600.0  33.0         9.0\n",
       "3  Sergio     Supply Chain        WL1     NaN  41.0        17.0\n",
       "4   Julia          Finance       None  7200.0  22.0         1.0"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees = pd.DataFrame({\"Name\":[\"Josh\",None,\"Julia\",\"Sergio\",\"Julia\",\"Michael\",\"Sarath\",np.nan,\"Chris\"],\n",
    "                          \"Department\":[\"IT\",\"Human Resources\",\"Finance\",\"Supply Chain\",\"Finance\",\"Marketing\",\"IT\",\"Human Resources\",\"Law\"],\n",
    "                          \"Work Level\":[\"WL3\",\"WL2\",\"WL2\",\"WL1\",None,\"WL2\",\"WL1\",\"WL3\",\"WL1\"],\n",
    "                          \"Income\":[4800,5200,6600,np.nan,7200,8400,np.nan,4200,9400],\n",
    "                          \"Age\":[24,28,33,41,22,46,31,None,39],\n",
    "                          \"Experience\":[2,np.nan,9,17,1,24,10,6,13]})\n",
    "employees.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entries missing values are given the value **NaN**, short for \"Not a Number\". For technical reasons these NaN values are always of the float64 dtype.\n",
    "\n",
    "Pandas provides some methods specific to missing data. To select NaN entries, we can use pd.isnull()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "5    False\n",
       "6     True\n",
       "7    False\n",
       "8    False\n",
       "Name: Income, dtype: bool"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[\"Income\"].isnull() #It returns true if row has nan value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Work Level</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>WL1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>IT</td>\n",
       "      <td>WL1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Department Work Level  Income   Age  Experience\n",
       "3  Sergio  Supply Chain        WL1     NaN  41.0        17.0\n",
       "6  Sarath            IT        WL1     NaN  31.0        10.0"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees[employees[\"Income\"].isnull()] #We can select values which their income is nan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to fill missing values(nan), we can use **fillna()** method by giving spesific value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Work Level</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>WL1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>IT</td>\n",
       "      <td>WL1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Department Work Level  Income   Age  Experience\n",
       "3  Sergio  Supply Chain        WL1     NaN  41.0        17.0\n",
       "6  Sarath            IT        WL1     NaN  31.0        10.0"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_nan = employees[employees[\"Income\"].isnull()]\n",
    "income_nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Work Level</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>WL1</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>IT</td>\n",
       "      <td>WL1</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Department Work Level  Income   Age  Experience\n",
       "3  Sergio  Supply Chain        WL1  1000.0  41.0        17.0\n",
       "6  Sarath            IT        WL1  1000.0  31.0        10.0"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_nan.fillna(1000) #It will fill all na values with 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new income values are float. Because nan values were float. We can change type after filling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Work Level</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>WL1</td>\n",
       "      <td>6542.857143</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>IT</td>\n",
       "      <td>WL1</td>\n",
       "      <td>6542.857143</td>\n",
       "      <td>31.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Department Work Level       Income   Age  Experience\n",
       "3  Sergio  Supply Chain        WL1  6542.857143  41.0        17.0\n",
       "6  Sarath            IT        WL1  6542.857143  31.0        10.0"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_nan.fillna(employees[\"Income\"].mean()) #We can also give a statistical value. It is better generally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Work Level</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>WL1</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>IT</td>\n",
       "      <td>WL1</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Department Work Level  Income   Age  Experience\n",
       "3  Sergio  Supply Chain        WL1  6600.0  41.0        17.0\n",
       "6  Sarath            IT        WL1  6600.0  31.0        10.0"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_nan.fillna(employees[\"Income\"].median()) #We can also give a statistical value. It is better generally."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also directly drop missing values with **dropna()** method. But in general, it is not preferred because it delete also information about dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>WL3</td>\n",
       "      <td>4800.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>WL2</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>WL2</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>WL1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>None</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>WL2</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>IT</td>\n",
       "      <td>WL1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>WL3</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Law</td>\n",
       "      <td>WL1</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name       Department Work Level  Income   Age  Experience\n",
       "0     Josh               IT        WL3  4800.0  24.0         2.0\n",
       "1     None  Human Resources        WL2  5200.0  28.0         NaN\n",
       "2    Julia          Finance        WL2  6600.0  33.0         9.0\n",
       "3   Sergio     Supply Chain        WL1     NaN  41.0        17.0\n",
       "4    Julia          Finance       None  7200.0  22.0         1.0\n",
       "5  Michael        Marketing        WL2  8400.0  46.0        24.0\n",
       "6   Sarath               IT        WL1     NaN  31.0        10.0\n",
       "7      NaN  Human Resources        WL3  4200.0   NaN         6.0\n",
       "8    Chris              Law        WL1  9400.0  39.0        13.0"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Work Level</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
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       "      <th>0</th>\n",
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       "      <td>WL3</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>WL2</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Michael</td>\n",
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       "      <td>8400.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Law</td>\n",
       "      <td>WL1</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name Department Work Level  Income   Age  Experience\n",
       "0     Josh         IT        WL3  4800.0  24.0         2.0\n",
       "2    Julia    Finance        WL2  6600.0  33.0         9.0\n",
       "5  Michael  Marketing        WL2  8400.0  46.0        24.0\n",
       "8    Chris        Law        WL1  9400.0  39.0        13.0"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.dropna() # It drops all na values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Department</th>\n",
       "      <th>Work Level</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh</td>\n",
       "      <td>IT</td>\n",
       "      <td>WL3</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>WL2</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>WL2</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sergio</td>\n",
       "      <td>Supply Chain</td>\n",
       "      <td>WL1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Finance</td>\n",
       "      <td>None</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>WL2</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sarath</td>\n",
       "      <td>IT</td>\n",
       "      <td>WL1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>WL3</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Law</td>\n",
       "      <td>WL1</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name       Department Work Level  Income   Age  Experience\n",
       "0     Josh               IT        WL3  4800.0  24.0         2.0\n",
       "1     None  Human Resources        WL2  5200.0  28.0         NaN\n",
       "2    Julia          Finance        WL2  6600.0  33.0         9.0\n",
       "3   Sergio     Supply Chain        WL1     NaN  41.0        17.0\n",
       "4    Julia          Finance       None  7200.0  22.0         1.0\n",
       "5  Michael        Marketing        WL2  8400.0  46.0        24.0\n",
       "6   Sarath               IT        WL1     NaN  31.0        10.0\n",
       "7      NaN  Human Resources        WL3  4200.0   NaN         6.0\n",
       "8    Chris              Law        WL1  9400.0  39.0        13.0"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.dropna(how=\"all\") # It drops all na values if all variables in a row is na."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Supply Chain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Law</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Department\n",
       "0               IT\n",
       "1  Human Resources\n",
       "2          Finance\n",
       "3     Supply Chain\n",
       "4          Finance\n",
       "5        Marketing\n",
       "6               IT\n",
       "7  Human Resources\n",
       "8              Law"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees.dropna(axis=\"columns\") #We can drop columns if it has na value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working external files in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple way to store big data sets is to use external files. These files can be csv(comma separated files), excel,txt or any other file. Pandas gives possibility to read these files with functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading csv files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CSV files contains plain text and is a well know format that can be read by everyone including Pandas. We will work with Netflix Movies and TV Shows dataset. You can download it from here:\n",
    "\n",
    "https://www.kaggle.com/shivamb/netflix-shows\n",
    "\n",
    "The name of the file is **\"netflix_titles.csv\"**. So we will read it with **read_csv()** function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>show_id</th>\n",
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       "      <td>81145628</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Norm of the North: King Sized Adventure</td>\n",
       "      <td>Richard Finn, Tim Maltby</td>\n",
       "      <td>Alan Marriott, Andrew Toth, Brian Dobson, Cole...</td>\n",
       "      <td>United States, India, South Korea, China</td>\n",
       "      <td>September 9, 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>TV-PG</td>\n",
       "      <td>90 min</td>\n",
       "      <td>Children &amp; Family Movies, Comedies</td>\n",
       "      <td>Before planning an awesome wedding for his gra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80117401</td>\n",
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       "      <td>Jandino: Whatever it Takes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jandino Asporaat</td>\n",
       "      <td>United Kingdom</td>\n",
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       "      <td>TV-MA</td>\n",
       "      <td>94 min</td>\n",
       "      <td>Stand-Up Comedy</td>\n",
       "      <td>Jandino Asporaat riffs on the challenges of ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70234439</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Transformers Prime</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peter Cullen, Sumalee Montano, Frank Welker, J...</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 8, 2018</td>\n",
       "      <td>2013</td>\n",
       "      <td>TV-Y7-FV</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Kids' TV</td>\n",
       "      <td>With the help of three human allies, the Autob...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80058654</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Transformers: Robots in Disguise</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>United States</td>\n",
       "      <td>September 8, 2018</td>\n",
       "      <td>2016</td>\n",
       "      <td>TV-Y7</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Kids' TV</td>\n",
       "      <td>When a prison ship crash unleashes hundreds of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80125979</td>\n",
       "      <td>Movie</td>\n",
       "      <td>#realityhigh</td>\n",
       "      <td>Fernando Lebrija</td>\n",
       "      <td>Nesta Cooper, Kate Walsh, John Michael Higgins...</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 8, 2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>99 min</td>\n",
       "      <td>Comedies</td>\n",
       "      <td>When nerdy high schooler Dani finally attracts...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>6229</th>\n",
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       "      <td>Red vs. Blue</td>\n",
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       "      <td>Burnie Burns, Jason Saldaña, Gustavo Sorola, G...</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>NR</td>\n",
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       "      <td>TV Action &amp; Adventure, TV Comedies, TV Sci-Fi ...</td>\n",
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       "      <td>Marc Maron, Judd Hirsch, Josh Brener, Nora Zeh...</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>4 Seasons</td>\n",
       "      <td>TV Comedies</td>\n",
       "      <td>Marc Maron stars as Marc Maron, who interviews...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6231</th>\n",
       "      <td>80116008</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Little Baby Bum: Nursery Rhyme Friends</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60 min</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Nursery rhymes and original music for children...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6232</th>\n",
       "      <td>70281022</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>A Young Doctor's Notebook and Other Stories</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Daniel Radcliffe, Jon Hamm, Adam Godley, Chris...</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>British TV Shows, TV Comedies, TV Dramas</td>\n",
       "      <td>Set during the Russian Revolution, this comic ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6233</th>\n",
       "      <td>70153404</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Friends</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jennifer Aniston, Courteney Cox, Lisa Kudrow, ...</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>10 Seasons</td>\n",
       "      <td>Classic &amp; Cult TV, TV Comedies</td>\n",
       "      <td>This hit sitcom follows the merry misadventure...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6234 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       show_id     type                                        title  \\\n",
       "0     81145628    Movie      Norm of the North: King Sized Adventure   \n",
       "1     80117401    Movie                   Jandino: Whatever it Takes   \n",
       "2     70234439  TV Show                           Transformers Prime   \n",
       "3     80058654  TV Show             Transformers: Robots in Disguise   \n",
       "4     80125979    Movie                                 #realityhigh   \n",
       "...        ...      ...                                          ...   \n",
       "6229  80000063  TV Show                                 Red vs. Blue   \n",
       "6230  70286564  TV Show                                        Maron   \n",
       "6231  80116008    Movie       Little Baby Bum: Nursery Rhyme Friends   \n",
       "6232  70281022  TV Show  A Young Doctor's Notebook and Other Stories   \n",
       "6233  70153404  TV Show                                      Friends   \n",
       "\n",
       "                      director  \\\n",
       "0     Richard Finn, Tim Maltby   \n",
       "1                          NaN   \n",
       "2                          NaN   \n",
       "3                          NaN   \n",
       "4             Fernando Lebrija   \n",
       "...                        ...   \n",
       "6229                       NaN   \n",
       "6230                       NaN   \n",
       "6231                       NaN   \n",
       "6232                       NaN   \n",
       "6233                       NaN   \n",
       "\n",
       "                                                   cast  \\\n",
       "0     Alan Marriott, Andrew Toth, Brian Dobson, Cole...   \n",
       "1                                      Jandino Asporaat   \n",
       "2     Peter Cullen, Sumalee Montano, Frank Welker, J...   \n",
       "3     Will Friedle, Darren Criss, Constance Zimmer, ...   \n",
       "4     Nesta Cooper, Kate Walsh, John Michael Higgins...   \n",
       "...                                                 ...   \n",
       "6229  Burnie Burns, Jason Saldaña, Gustavo Sorola, G...   \n",
       "6230  Marc Maron, Judd Hirsch, Josh Brener, Nora Zeh...   \n",
       "6231                                                NaN   \n",
       "6232  Daniel Radcliffe, Jon Hamm, Adam Godley, Chris...   \n",
       "6233  Jennifer Aniston, Courteney Cox, Lisa Kudrow, ...   \n",
       "\n",
       "                                       country         date_added  \\\n",
       "0     United States, India, South Korea, China  September 9, 2019   \n",
       "1                               United Kingdom  September 9, 2016   \n",
       "2                                United States  September 8, 2018   \n",
       "3                                United States  September 8, 2018   \n",
       "4                                United States  September 8, 2017   \n",
       "...                                        ...                ...   \n",
       "6229                             United States                NaN   \n",
       "6230                             United States                NaN   \n",
       "6231                                       NaN                NaN   \n",
       "6232                            United Kingdom                NaN   \n",
       "6233                             United States                NaN   \n",
       "\n",
       "      release_year    rating    duration  \\\n",
       "0             2019     TV-PG      90 min   \n",
       "1             2016     TV-MA      94 min   \n",
       "2             2013  TV-Y7-FV    1 Season   \n",
       "3             2016     TV-Y7    1 Season   \n",
       "4             2017     TV-14      99 min   \n",
       "...            ...       ...         ...   \n",
       "6229          2015        NR  13 Seasons   \n",
       "6230          2016     TV-MA   4 Seasons   \n",
       "6231          2016       NaN      60 min   \n",
       "6232          2013     TV-MA   2 Seasons   \n",
       "6233          2003     TV-14  10 Seasons   \n",
       "\n",
       "                                              listed_in  \\\n",
       "0                    Children & Family Movies, Comedies   \n",
       "1                                       Stand-Up Comedy   \n",
       "2                                              Kids' TV   \n",
       "3                                              Kids' TV   \n",
       "4                                              Comedies   \n",
       "...                                                 ...   \n",
       "6229  TV Action & Adventure, TV Comedies, TV Sci-Fi ...   \n",
       "6230                                        TV Comedies   \n",
       "6231                                             Movies   \n",
       "6232           British TV Shows, TV Comedies, TV Dramas   \n",
       "6233                     Classic & Cult TV, TV Comedies   \n",
       "\n",
       "                                            description  \n",
       "0     Before planning an awesome wedding for his gra...  \n",
       "1     Jandino Asporaat riffs on the challenges of ra...  \n",
       "2     With the help of three human allies, the Autob...  \n",
       "3     When a prison ship crash unleashes hundreds of...  \n",
       "4     When nerdy high schooler Dani finally attracts...  \n",
       "...                                                 ...  \n",
       "6229  This parody of first-person shooter games, mil...  \n",
       "6230  Marc Maron stars as Marc Maron, who interviews...  \n",
       "6231  Nursery rhymes and original music for children...  \n",
       "6232  Set during the Russian Revolution, this comic ...  \n",
       "6233  This hit sitcom follows the merry misadventure...  \n",
       "\n",
       "[6234 rows x 12 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"netflix_titles.csv\") #We are giving path of data. In this example, path is same with this file.\n",
    "#It's default separeter is ,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>show_id</th>\n",
       "      <th>type</th>\n",
       "      <th>title</th>\n",
       "      <th>director</th>\n",
       "      <th>cast</th>\n",
       "      <th>country</th>\n",
       "      <th>date_added</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81145628</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Norm of the North: King Sized Adventure</td>\n",
       "      <td>Richard Finn, Tim Maltby</td>\n",
       "      <td>Alan Marriott, Andrew Toth, Brian Dobson, Cole...</td>\n",
       "      <td>United States, India, South Korea, China</td>\n",
       "      <td>September 9, 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>TV-PG</td>\n",
       "      <td>90 min</td>\n",
       "      <td>Children &amp; Family Movies, Comedies</td>\n",
       "      <td>Before planning an awesome wedding for his gra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80117401</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Jandino: Whatever it Takes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jandino Asporaat</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>September 9, 2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>94 min</td>\n",
       "      <td>Stand-Up Comedy</td>\n",
       "      <td>Jandino Asporaat riffs on the challenges of ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70234439</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Transformers Prime</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peter Cullen, Sumalee Montano, Frank Welker, J...</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 8, 2018</td>\n",
       "      <td>2013</td>\n",
       "      <td>TV-Y7-FV</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Kids' TV</td>\n",
       "      <td>With the help of three human allies, the Autob...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80058654</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Transformers: Robots in Disguise</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Will Friedle, Darren Criss, Constance Zimmer, ...</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 8, 2018</td>\n",
       "      <td>2016</td>\n",
       "      <td>TV-Y7</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Kids' TV</td>\n",
       "      <td>When a prison ship crash unleashes hundreds of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80125979</td>\n",
       "      <td>Movie</td>\n",
       "      <td>#realityhigh</td>\n",
       "      <td>Fernando Lebrija</td>\n",
       "      <td>Nesta Cooper, Kate Walsh, John Michael Higgins...</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 8, 2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>99 min</td>\n",
       "      <td>Comedies</td>\n",
       "      <td>When nerdy high schooler Dani finally attracts...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    show_id     type                                    title  \\\n",
       "0  81145628    Movie  Norm of the North: King Sized Adventure   \n",
       "1  80117401    Movie               Jandino: Whatever it Takes   \n",
       "2  70234439  TV Show                       Transformers Prime   \n",
       "3  80058654  TV Show         Transformers: Robots in Disguise   \n",
       "4  80125979    Movie                             #realityhigh   \n",
       "\n",
       "                   director  \\\n",
       "0  Richard Finn, Tim Maltby   \n",
       "1                       NaN   \n",
       "2                       NaN   \n",
       "3                       NaN   \n",
       "4          Fernando Lebrija   \n",
       "\n",
       "                                                cast  \\\n",
       "0  Alan Marriott, Andrew Toth, Brian Dobson, Cole...   \n",
       "1                                   Jandino Asporaat   \n",
       "2  Peter Cullen, Sumalee Montano, Frank Welker, J...   \n",
       "3  Will Friedle, Darren Criss, Constance Zimmer, ...   \n",
       "4  Nesta Cooper, Kate Walsh, John Michael Higgins...   \n",
       "\n",
       "                                    country         date_added  release_year  \\\n",
       "0  United States, India, South Korea, China  September 9, 2019          2019   \n",
       "1                            United Kingdom  September 9, 2016          2016   \n",
       "2                             United States  September 8, 2018          2013   \n",
       "3                             United States  September 8, 2018          2016   \n",
       "4                             United States  September 8, 2017          2017   \n",
       "\n",
       "     rating  duration                           listed_in  \\\n",
       "0     TV-PG    90 min  Children & Family Movies, Comedies   \n",
       "1     TV-MA    94 min                     Stand-Up Comedy   \n",
       "2  TV-Y7-FV  1 Season                            Kids' TV   \n",
       "3     TV-Y7  1 Season                            Kids' TV   \n",
       "4     TV-14    99 min                            Comedies   \n",
       "\n",
       "                                         description  \n",
       "0  Before planning an awesome wedding for his gra...  \n",
       "1  Jandino Asporaat riffs on the challenges of ra...  \n",
       "2  With the help of three human allies, the Autob...  \n",
       "3  When a prison ship crash unleashes hundreds of...  \n",
       "4  When nerdy high schooler Dani finally attracts...  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix = pd.read_csv(\"netflix_titles.csv\") #We assign it to \"netflix\" variable\n",
    "netflix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading excel(xlsx) files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For xlsx,we will use \"Crime in England\" dataset. Data from the Crime Survey for England and Wales (CSEW) showing breakdowns of victimisation over time and by various demographic characteristics. Please note: The methodology by which the CSEW calculates its incidents of crime changed in December 2018. You can download it from here:\n",
    "\n",
    "https://www.ons.gov.uk/peoplepopulationandcommunity/crimeandjustice/datasets/crimeinenglandandwalesannualtrendanddemographictables\n",
    "\n",
    "The name of the file is **\"crimeinengland.xlsx\"**. So we will read it with **read_excel()** function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crime in England and Wales: year ending March 2020 - Annual trend and demographic tables</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Notes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>These data tables are published alongside the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>For explanatory notes on these statistics, see...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Data tables shown in this workbook relate to t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Following a methodological change to the handl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Fieldwork for the year to March 2020 was suspe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>The data contained in this file comprise:</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Table D1: Proportion of adults who were victim...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Table D2: Proportion of adults who were victim...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Table D3: Proportion of adults who were victim...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Table D4: Proportion of adults who were victim...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Table D5: Proportion of children aged 10 to 15...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Table D6: Proportion of children aged 10 to 15...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Table D7: Number of times victims were victimi...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Table D8: Proportion of victims who were victi...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Table D9: Proportion of incidents experienced ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Table D10: Percentage of CSEW incidents report...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Table D11: Reasons for not reporting incidents...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Table D12: Reasons for not reporting incidents...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Table D13: Trends in experience of anti-social...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Table D14: Experience of anti-social behaviour...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Table D15: Experience of anti-social behaviour...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>For further information about the Crime Survey...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>or write to: ONS Centre for Crime and Justice...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Statistical contact: Nicholas Stripe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Tel: +44 (0)20 7592 8695</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Email: crimestatistics@ons.gov.uk</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Crime in England and Wales: year ending March 2020 - Annual trend and demographic tables  \\\n",
       "0                                                 NaN                                         \n",
       "1                                                 NaN                                         \n",
       "2                                                 NaN                                         \n",
       "3                                                 NaN                                         \n",
       "4                                                 NaN                                         \n",
       "5                                                 NaN                                         \n",
       "6                                                 NaN                                         \n",
       "7                                                 NaN                                         \n",
       "8                                                 NaN                                         \n",
       "9                                                 NaN                                         \n",
       "10                                                NaN                                         \n",
       "11                                                NaN                                         \n",
       "12          The data contained in this file comprise:                                         \n",
       "13  Table D1: Proportion of adults who were victim...                                         \n",
       "14  Table D2: Proportion of adults who were victim...                                         \n",
       "15  Table D3: Proportion of adults who were victim...                                         \n",
       "16  Table D4: Proportion of adults who were victim...                                         \n",
       "17  Table D5: Proportion of children aged 10 to 15...                                         \n",
       "18  Table D6: Proportion of children aged 10 to 15...                                         \n",
       "19  Table D7: Number of times victims were victimi...                                         \n",
       "20  Table D8: Proportion of victims who were victi...                                         \n",
       "21  Table D9: Proportion of incidents experienced ...                                         \n",
       "22  Table D10: Percentage of CSEW incidents report...                                         \n",
       "23  Table D11: Reasons for not reporting incidents...                                         \n",
       "24  Table D12: Reasons for not reporting incidents...                                         \n",
       "25  Table D13: Trends in experience of anti-social...                                         \n",
       "26  Table D14: Experience of anti-social behaviour...                                         \n",
       "27  Table D15: Experience of anti-social behaviour...                                         \n",
       "28                                                NaN                                         \n",
       "29  For further information about the Crime Survey...                                         \n",
       "30   or write to: ONS Centre for Crime and Justice...                                         \n",
       "31                                                NaN                                         \n",
       "32               Statistical contact: Nicholas Stripe                                         \n",
       "33                           Tel: +44 (0)20 7592 8695                                         \n",
       "34                  Email: crimestatistics@ons.gov.uk                                         \n",
       "\n",
       "                                           Unnamed: 1  \n",
       "0                                                 NaN  \n",
       "1                                               Notes  \n",
       "2   These data tables are published alongside the ...  \n",
       "3                                                 NaN  \n",
       "4   For explanatory notes on these statistics, see...  \n",
       "5                                                 NaN  \n",
       "6   Data tables shown in this workbook relate to t...  \n",
       "7                                                 NaN  \n",
       "8   Following a methodological change to the handl...  \n",
       "9                                                 NaN  \n",
       "10  Fieldwork for the year to March 2020 was suspe...  \n",
       "11                                                NaN  \n",
       "12                                                NaN  \n",
       "13                                                NaN  \n",
       "14                                                NaN  \n",
       "15                                                NaN  \n",
       "16                                                NaN  \n",
       "17                                                NaN  \n",
       "18                                                NaN  \n",
       "19                                                NaN  \n",
       "20                                                NaN  \n",
       "21                                                NaN  \n",
       "22                                                NaN  \n",
       "23                                                NaN  \n",
       "24                                                NaN  \n",
       "25                                                NaN  \n",
       "26                                                NaN  \n",
       "27                                                NaN  \n",
       "28                                                NaN  \n",
       "29                                                NaN  \n",
       "30                                                NaN  \n",
       "31                                                NaN  \n",
       "32                                                NaN  \n",
       "33                                                NaN  \n",
       "34                                                NaN  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel(\"crimeinengland.xlsx\")#We are giving path of data. In this example, path is same with this file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, dataset seems not right because it has different sheets. In order to specify a sheet, we'll use **sheet_name** argument. We can get sheets with this way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Notes',\n",
       " 'Table D1',\n",
       " 'Table D2',\n",
       " 'Table D3',\n",
       " 'Table D4',\n",
       " 'Table D5',\n",
       " 'Table D6',\n",
       " 'Table D7',\n",
       " 'Table D8',\n",
       " 'Table D9',\n",
       " 'Table D10',\n",
       " 'Table D11',\n",
       " 'Table D12',\n",
       " 'Table D13',\n",
       " 'Table D14',\n",
       " 'Table D15']"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes = pd.ExcelFile(\"crimeinengland.xlsx\")\n",
    "crimes.sheet_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Table D1: Proportion of adults who were victims of all CSEW crime and personal crime (including fraud and computer misuse), by personal characteristics, year ending March 2020 CSEW1,2</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>England and Wales</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adults aged 16 and over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Personal characteristic3</td>\n",
       "      <td>All CSEW crime (including fraud and computer m...</td>\n",
       "      <td>Personal crime (including fraud and computer m...</td>\n",
       "      <td>Unweighted base - number of adults</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Personal characteristic3</td>\n",
       "      <td>All CSEW crime (including fraud and computer m...</td>\n",
       "      <td>Personal crime (including fraud and computer m...</td>\n",
       "      <td>Unweighted base - number of adults</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Percentage, victims once or more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Percentage, victims once or more</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALL ADULTS</td>\n",
       "      <td>19.2891</td>\n",
       "      <td>11.0121</td>\n",
       "      <td>33735</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Respondent's employment status</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>7. The definition of disability used is consis...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>8. The terminology used to label this data has...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>9. ‘Gender identity the same as sex registered...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>10. A question on gender identity was added to...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>\"-\" indicates that data are not reported becau...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Table D1: Proportion of adults who were victims of all CSEW crime and personal crime (including fraud and computer misuse), by personal characteristics, year ending March 2020 CSEW1,2  \\\n",
       "0                                                 NaN                                                                                                                                        \n",
       "1                                   England and Wales                                                                                                                                        \n",
       "2                            Personal characteristic3                                                                                                                                        \n",
       "3                                                 NaN                                                                                                                                        \n",
       "4                                          ALL ADULTS                                                                                                                                        \n",
       "..                                                ...                                                                                                                                        \n",
       "77  7. The definition of disability used is consis...                                                                                                                                        \n",
       "78  8. The terminology used to label this data has...                                                                                                                                        \n",
       "79  9. ‘Gender identity the same as sex registered...                                                                                                                                        \n",
       "80  10. A question on gender identity was added to...                                                                                                                                        \n",
       "81  \"-\" indicates that data are not reported becau...                                                                                                                                        \n",
       "\n",
       "                                           Unnamed: 1  \\\n",
       "0                                                 NaN   \n",
       "1                                                 NaN   \n",
       "2   All CSEW crime (including fraud and computer m...   \n",
       "3                    Percentage, victims once or more   \n",
       "4                                             19.2891   \n",
       "..                                                ...   \n",
       "77                                                NaN   \n",
       "78                                                NaN   \n",
       "79                                                NaN   \n",
       "80                                                NaN   \n",
       "81                                                NaN   \n",
       "\n",
       "                                           Unnamed: 2  \\\n",
       "0                                                 NaN   \n",
       "1                                                 NaN   \n",
       "2   Personal crime (including fraud and computer m...   \n",
       "3                                                 NaN   \n",
       "4                                             11.0121   \n",
       "..                                                ...   \n",
       "77                                                NaN   \n",
       "78                                                NaN   \n",
       "79                                                NaN   \n",
       "80                                                NaN   \n",
       "81                                                NaN   \n",
       "\n",
       "                            Unnamed: 3  Unnamed: 4  \\\n",
       "0                                  NaN         NaN   \n",
       "1                                  NaN         NaN   \n",
       "2   Unweighted base - number of adults         NaN   \n",
       "3                                  NaN         NaN   \n",
       "4                                33735         NaN   \n",
       "..                                 ...         ...   \n",
       "77                                 NaN         NaN   \n",
       "78                                 NaN         NaN   \n",
       "79                                 NaN         NaN   \n",
       "80                                 NaN         NaN   \n",
       "81                                 NaN         NaN   \n",
       "\n",
       "                        Unnamed: 5  \\\n",
       "0                              NaN   \n",
       "1                              NaN   \n",
       "2         Personal characteristic3   \n",
       "3                              NaN   \n",
       "4   Respondent's employment status   \n",
       "..                             ...   \n",
       "77                             NaN   \n",
       "78                             NaN   \n",
       "79                             NaN   \n",
       "80                             NaN   \n",
       "81                             NaN   \n",
       "\n",
       "                                           Unnamed: 6  \\\n",
       "0                                                 NaN   \n",
       "1                                                 NaN   \n",
       "2   All CSEW crime (including fraud and computer m...   \n",
       "3                    Percentage, victims once or more   \n",
       "4                                                 NaN   \n",
       "..                                                ...   \n",
       "77                                                NaN   \n",
       "78                                                NaN   \n",
       "79                                                NaN   \n",
       "80                                                NaN   \n",
       "81                                                NaN   \n",
       "\n",
       "                                           Unnamed: 7  \\\n",
       "0                                                 NaN   \n",
       "1                                                 NaN   \n",
       "2   Personal crime (including fraud and computer m...   \n",
       "3                                                 NaN   \n",
       "4                                                 NaN   \n",
       "..                                                ...   \n",
       "77                                                NaN   \n",
       "78                                                NaN   \n",
       "79                                                NaN   \n",
       "80                                                NaN   \n",
       "81                                                NaN   \n",
       "\n",
       "                            Unnamed: 8  \n",
       "0                                  NaN  \n",
       "1              Adults aged 16 and over  \n",
       "2   Unweighted base - number of adults  \n",
       "3                                  NaN  \n",
       "4                                  NaN  \n",
       "..                                 ...  \n",
       "77                                 NaN  \n",
       "78                                 NaN  \n",
       "79                                 NaN  \n",
       "80                                 NaN  \n",
       "81                                 NaN  \n",
       "\n",
       "[82 rows x 9 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel(\"crimeinengland.xlsx\",sheet_name=\"Table D1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Netflix Dataset (basic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, we will try to understand Netflix Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80125979</td>\n",
       "      <td>Movie</td>\n",
       "      <td>#realityhigh</td>\n",
       "      <td>Fernando Lebrija</td>\n",
       "      <td>Nesta Cooper, Kate Walsh, John Michael Higgins...</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 8, 2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>99 min</td>\n",
       "      <td>Comedies</td>\n",
       "      <td>When nerdy high schooler Dani finally attracts...</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "    show_id     type                                    title  \\\n",
       "0  81145628    Movie  Norm of the North: King Sized Adventure   \n",
       "1  80117401    Movie               Jandino: Whatever it Takes   \n",
       "2  70234439  TV Show                       Transformers Prime   \n",
       "3  80058654  TV Show         Transformers: Robots in Disguise   \n",
       "4  80125979    Movie                             #realityhigh   \n",
       "\n",
       "                   director  \\\n",
       "0  Richard Finn, Tim Maltby   \n",
       "1                       NaN   \n",
       "2                       NaN   \n",
       "3                       NaN   \n",
       "4          Fernando Lebrija   \n",
       "\n",
       "                                                cast  \\\n",
       "0  Alan Marriott, Andrew Toth, Brian Dobson, Cole...   \n",
       "1                                   Jandino Asporaat   \n",
       "2  Peter Cullen, Sumalee Montano, Frank Welker, J...   \n",
       "3  Will Friedle, Darren Criss, Constance Zimmer, ...   \n",
       "4  Nesta Cooper, Kate Walsh, John Michael Higgins...   \n",
       "\n",
       "                                    country         date_added  release_year  \\\n",
       "0  United States, India, South Korea, China  September 9, 2019          2019   \n",
       "1                            United Kingdom  September 9, 2016          2016   \n",
       "2                             United States  September 8, 2018          2013   \n",
       "3                             United States  September 8, 2018          2016   \n",
       "4                             United States  September 8, 2017          2017   \n",
       "\n",
       "     rating  duration                           listed_in  \\\n",
       "0     TV-PG    90 min  Children & Family Movies, Comedies   \n",
       "1     TV-MA    94 min                     Stand-Up Comedy   \n",
       "2  TV-Y7-FV  1 Season                            Kids' TV   \n",
       "3     TV-Y7  1 Season                            Kids' TV   \n",
       "4     TV-14    99 min                            Comedies   \n",
       "\n",
       "                                         description  \n",
       "0  Before planning an awesome wedding for his gra...  \n",
       "1  Jandino Asporaat riffs on the challenges of ra...  \n",
       "2  With the help of three human allies, the Autob...  \n",
       "3  When a prison ship crash unleashes hundreds of...  \n",
       "4  When nerdy high schooler Dani finally attracts...  "
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"netflix_titles.csv\") \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>6229</th>\n",
       "      <td>80000063</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Red vs. Blue</td>\n",
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       "      <td>TV Show</td>\n",
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       "      <td>Marc Maron, Judd Hirsch, Josh Brener, Nora Zeh...</td>\n",
       "      <td>United States</td>\n",
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       "      <td>TV-MA</td>\n",
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       "      <td>Marc Maron stars as Marc Maron, who interviews...</td>\n",
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       "      <td>60 min</td>\n",
       "      <td>Movies</td>\n",
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       "      <td>Set during the Russian Revolution, this comic ...</td>\n",
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       "      <td>70153404</td>\n",
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       "      <td>2003</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>10 Seasons</td>\n",
       "      <td>Classic &amp; Cult TV, TV Comedies</td>\n",
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      "text/plain": [
       "       show_id     type                                        title director  \\\n",
       "6229  80000063  TV Show                                 Red vs. Blue      NaN   \n",
       "6230  70286564  TV Show                                        Maron      NaN   \n",
       "6231  80116008    Movie       Little Baby Bum: Nursery Rhyme Friends      NaN   \n",
       "6232  70281022  TV Show  A Young Doctor's Notebook and Other Stories      NaN   \n",
       "6233  70153404  TV Show                                      Friends      NaN   \n",
       "\n",
       "                                                   cast         country  \\\n",
       "6229  Burnie Burns, Jason Saldaña, Gustavo Sorola, G...   United States   \n",
       "6230  Marc Maron, Judd Hirsch, Josh Brener, Nora Zeh...   United States   \n",
       "6231                                                NaN             NaN   \n",
       "6232  Daniel Radcliffe, Jon Hamm, Adam Godley, Chris...  United Kingdom   \n",
       "6233  Jennifer Aniston, Courteney Cox, Lisa Kudrow, ...   United States   \n",
       "\n",
       "     date_added  release_year rating    duration  \\\n",
       "6229        NaN          2015     NR  13 Seasons   \n",
       "6230        NaN          2016  TV-MA   4 Seasons   \n",
       "6231        NaN          2016    NaN      60 min   \n",
       "6232        NaN          2013  TV-MA   2 Seasons   \n",
       "6233        NaN          2003  TV-14  10 Seasons   \n",
       "\n",
       "                                              listed_in  \\\n",
       "6229  TV Action & Adventure, TV Comedies, TV Sci-Fi ...   \n",
       "6230                                        TV Comedies   \n",
       "6231                                             Movies   \n",
       "6232           British TV Shows, TV Comedies, TV Dramas   \n",
       "6233                     Classic & Cult TV, TV Comedies   \n",
       "\n",
       "                                            description  \n",
       "6229  This parody of first-person shooter games, mil...  \n",
       "6230  Marc Maron stars as Marc Maron, who interviews...  \n",
       "6231  Nursery rhymes and original music for children...  \n",
       "6232  Set during the Russian Revolution, this comic ...  \n",
       "6233  This hit sitcom follows the merry misadventure...  "
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6234, 12)"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of        show_id     type                                        title  \\\n",
       "0     81145628    Movie      Norm of the North: King Sized Adventure   \n",
       "1     80117401    Movie                   Jandino: Whatever it Takes   \n",
       "2     70234439  TV Show                           Transformers Prime   \n",
       "3     80058654  TV Show             Transformers: Robots in Disguise   \n",
       "4     80125979    Movie                                 #realityhigh   \n",
       "...        ...      ...                                          ...   \n",
       "6229  80000063  TV Show                                 Red vs. Blue   \n",
       "6230  70286564  TV Show                                        Maron   \n",
       "6231  80116008    Movie       Little Baby Bum: Nursery Rhyme Friends   \n",
       "6232  70281022  TV Show  A Young Doctor's Notebook and Other Stories   \n",
       "6233  70153404  TV Show                                      Friends   \n",
       "\n",
       "                      director  \\\n",
       "0     Richard Finn, Tim Maltby   \n",
       "1                          NaN   \n",
       "2                          NaN   \n",
       "3                          NaN   \n",
       "4             Fernando Lebrija   \n",
       "...                        ...   \n",
       "6229                       NaN   \n",
       "6230                       NaN   \n",
       "6231                       NaN   \n",
       "6232                       NaN   \n",
       "6233                       NaN   \n",
       "\n",
       "                                                   cast  \\\n",
       "0     Alan Marriott, Andrew Toth, Brian Dobson, Cole...   \n",
       "1                                      Jandino Asporaat   \n",
       "2     Peter Cullen, Sumalee Montano, Frank Welker, J...   \n",
       "3     Will Friedle, Darren Criss, Constance Zimmer, ...   \n",
       "4     Nesta Cooper, Kate Walsh, John Michael Higgins...   \n",
       "...                                                 ...   \n",
       "6229  Burnie Burns, Jason Saldaña, Gustavo Sorola, G...   \n",
       "6230  Marc Maron, Judd Hirsch, Josh Brener, Nora Zeh...   \n",
       "6231                                                NaN   \n",
       "6232  Daniel Radcliffe, Jon Hamm, Adam Godley, Chris...   \n",
       "6233  Jennifer Aniston, Courteney Cox, Lisa Kudrow, ...   \n",
       "\n",
       "                                       country         date_added  \\\n",
       "0     United States, India, South Korea, China  September 9, 2019   \n",
       "1                               United Kingdom  September 9, 2016   \n",
       "2                                United States  September 8, 2018   \n",
       "3                                United States  September 8, 2018   \n",
       "4                                United States  September 8, 2017   \n",
       "...                                        ...                ...   \n",
       "6229                             United States                NaN   \n",
       "6230                             United States                NaN   \n",
       "6231                                       NaN                NaN   \n",
       "6232                            United Kingdom                NaN   \n",
       "6233                             United States                NaN   \n",
       "\n",
       "      release_year    rating    duration  \\\n",
       "0             2019     TV-PG      90 min   \n",
       "1             2016     TV-MA      94 min   \n",
       "2             2013  TV-Y7-FV    1 Season   \n",
       "3             2016     TV-Y7    1 Season   \n",
       "4             2017     TV-14      99 min   \n",
       "...            ...       ...         ...   \n",
       "6229          2015        NR  13 Seasons   \n",
       "6230          2016     TV-MA   4 Seasons   \n",
       "6231          2016       NaN      60 min   \n",
       "6232          2013     TV-MA   2 Seasons   \n",
       "6233          2003     TV-14  10 Seasons   \n",
       "\n",
       "                                              listed_in  \\\n",
       "0                    Children & Family Movies, Comedies   \n",
       "1                                       Stand-Up Comedy   \n",
       "2                                              Kids' TV   \n",
       "3                                              Kids' TV   \n",
       "4                                              Comedies   \n",
       "...                                                 ...   \n",
       "6229  TV Action & Adventure, TV Comedies, TV Sci-Fi ...   \n",
       "6230                                        TV Comedies   \n",
       "6231                                             Movies   \n",
       "6232           British TV Shows, TV Comedies, TV Dramas   \n",
       "6233                     Classic & Cult TV, TV Comedies   \n",
       "\n",
       "                                            description  \n",
       "0     Before planning an awesome wedding for his gra...  \n",
       "1     Jandino Asporaat riffs on the challenges of ra...  \n",
       "2     With the help of three human allies, the Autob...  \n",
       "3     When a prison ship crash unleashes hundreds of...  \n",
       "4     When nerdy high schooler Dani finally attracts...  \n",
       "...                                                 ...  \n",
       "6229  This parody of first-person shooter games, mil...  \n",
       "6230  Marc Maron stars as Marc Maron, who interviews...  \n",
       "6231  Nursery rhymes and original music for children...  \n",
       "6232  Set during the Russian Revolution, this comic ...  \n",
       "6233  This hit sitcom follows the merry misadventure...  \n",
       "\n",
       "[6234 rows x 12 columns]>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "show_id          int64\n",
       "type            object\n",
       "title           object\n",
       "director        object\n",
       "cast            object\n",
       "country         object\n",
       "date_added      object\n",
       "release_year     int64\n",
       "rating          object\n",
       "duration        object\n",
       "listed_in       object\n",
       "description     object\n",
       "dtype: object"
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     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6.234000e+03</td>\n",
       "      <td>6234.00000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>7.670368e+07</td>\n",
       "      <td>2013.35932</td>\n",
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       "    <tr>\n",
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       "      <td>2020.00000</td>\n",
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       "            show_id  release_year\n",
       "count  6.234000e+03    6234.00000\n",
       "mean   7.670368e+07    2013.35932\n",
       "std    1.094296e+07       8.81162\n",
       "min    2.477470e+05    1925.00000\n",
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       "75%    8.024489e+07    2018.00000\n",
       "max    8.123573e+07    2020.00000"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**describe()** function does not contains categorical variables. In order to enable it, we will use include term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.016337e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.024489e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.123573e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             show_id   type        title                director  \\\n",
       "count   6.234000e+03   6234         6234                    4265   \n",
       "unique           NaN      2         6172                    3301   \n",
       "top              NaN  Movie  The Silence  Raúl Campos, Jan Suter   \n",
       "freq             NaN   4265            3                      18   \n",
       "mean    7.670368e+07    NaN          NaN                     NaN   \n",
       "std     1.094296e+07    NaN          NaN                     NaN   \n",
       "min     2.477470e+05    NaN          NaN                     NaN   \n",
       "25%     8.003580e+07    NaN          NaN                     NaN   \n",
       "50%     8.016337e+07    NaN          NaN                     NaN   \n",
       "75%     8.024489e+07    NaN          NaN                     NaN   \n",
       "max     8.123573e+07    NaN          NaN                     NaN   \n",
       "\n",
       "                      cast        country       date_added  release_year  \\\n",
       "count                 5664           5758             6223    6234.00000   \n",
       "unique                5469            554             1524           NaN   \n",
       "top     David Attenborough  United States  January 1, 2020           NaN   \n",
       "freq                    18           2032              122           NaN   \n",
       "mean                   NaN            NaN              NaN    2013.35932   \n",
       "std                    NaN            NaN              NaN       8.81162   \n",
       "min                    NaN            NaN              NaN    1925.00000   \n",
       "25%                    NaN            NaN              NaN    2013.00000   \n",
       "50%                    NaN            NaN              NaN    2016.00000   \n",
       "75%                    NaN            NaN              NaN    2018.00000   \n",
       "max                    NaN            NaN              NaN    2020.00000   \n",
       "\n",
       "       rating  duration      listed_in  \\\n",
       "count    6224      6234           6234   \n",
       "unique     14       201            461   \n",
       "top     TV-MA  1 Season  Documentaries   \n",
       "freq     2027      1321            299   \n",
       "mean      NaN       NaN            NaN   \n",
       "std       NaN       NaN            NaN   \n",
       "min       NaN       NaN            NaN   \n",
       "25%       NaN       NaN            NaN   \n",
       "50%       NaN       NaN            NaN   \n",
       "75%       NaN       NaN            NaN   \n",
       "max       NaN       NaN            NaN   \n",
       "\n",
       "                                              description  \n",
       "count                                                6234  \n",
       "unique                                               6226  \n",
       "top     A surly septuagenarian gets another chance at ...  \n",
       "freq                                                    3  \n",
       "mean                                                  NaN  \n",
       "std                                                   NaN  \n",
       "min                                                   NaN  \n",
       "25%                                                   NaN  \n",
       "50%                                                   NaN  \n",
       "75%                                                   NaN  \n",
       "max                                                   NaN  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=[object, 'category',\"int64\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "show_id            0\n",
       "type               0\n",
       "title              0\n",
       "director        1969\n",
       "cast             570\n",
       "country          476\n",
       "date_added        11\n",
       "release_year       0\n",
       "rating            10\n",
       "duration           0\n",
       "listed_in          0\n",
       "description        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum() # Summing NA values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['show_id', 'type', 'title', 'director', 'cast', 'country', 'date_added',\n",
       "       'release_year', 'rating', 'duration', 'listed_in', 'description'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "show_id         6234\n",
       "type               2\n",
       "title           6172\n",
       "director        3301\n",
       "cast            5469\n",
       "country          554\n",
       "date_added      1524\n",
       "release_year      72\n",
       "rating            14\n",
       "duration         201\n",
       "listed_in        461\n",
       "description     6226\n",
       "dtype: int64"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()#It gives number of unique values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that show_id is unique for each row, so we will assign it as index value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81145628</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Norm of the North: King Sized Adventure</td>\n",
       "      <td>Richard Finn, Tim Maltby</td>\n",
       "      <td>Alan Marriott, Andrew Toth, Brian Dobson, Cole...</td>\n",
       "      <td>United States, India, South Korea, China</td>\n",
       "      <td>September 9, 2019</td>\n",
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       "      <td>Before planning an awesome wedding for his gra...</td>\n",
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       "      <th>80117401</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Jandino: Whatever it Takes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jandino Asporaat</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>September 9, 2016</td>\n",
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       "      <td>Stand-Up Comedy</td>\n",
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       "    <tr>\n",
       "      <th>70234439</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>Transformers Prime</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peter Cullen, Sumalee Montano, Frank Welker, J...</td>\n",
       "      <td>United States</td>\n",
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       "      <td>2013</td>\n",
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       "      <td>With the help of three human allies, the Autob...</td>\n",
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       "      <th>80058654</th>\n",
       "      <td>TV Show</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Will Friedle, Darren Criss, Constance Zimmer, ...</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 8, 2018</td>\n",
       "      <td>2016</td>\n",
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       "      <td>1 Season</td>\n",
       "      <td>Kids' TV</td>\n",
       "      <td>When a prison ship crash unleashes hundreds of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80125979</th>\n",
       "      <td>Movie</td>\n",
       "      <td>#realityhigh</td>\n",
       "      <td>Fernando Lebrija</td>\n",
       "      <td>Nesta Cooper, Kate Walsh, John Michael Higgins...</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 8, 2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>99 min</td>\n",
       "      <td>Comedies</td>\n",
       "      <td>When nerdy high schooler Dani finally attracts...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             type                                    title  \\\n",
       "show_id                                                      \n",
       "81145628    Movie  Norm of the North: King Sized Adventure   \n",
       "80117401    Movie               Jandino: Whatever it Takes   \n",
       "70234439  TV Show                       Transformers Prime   \n",
       "80058654  TV Show         Transformers: Robots in Disguise   \n",
       "80125979    Movie                             #realityhigh   \n",
       "\n",
       "                          director  \\\n",
       "show_id                              \n",
       "81145628  Richard Finn, Tim Maltby   \n",
       "80117401                       NaN   \n",
       "70234439                       NaN   \n",
       "80058654                       NaN   \n",
       "80125979          Fernando Lebrija   \n",
       "\n",
       "                                                       cast  \\\n",
       "show_id                                                       \n",
       "81145628  Alan Marriott, Andrew Toth, Brian Dobson, Cole...   \n",
       "80117401                                   Jandino Asporaat   \n",
       "70234439  Peter Cullen, Sumalee Montano, Frank Welker, J...   \n",
       "80058654  Will Friedle, Darren Criss, Constance Zimmer, ...   \n",
       "80125979  Nesta Cooper, Kate Walsh, John Michael Higgins...   \n",
       "\n",
       "                                           country         date_added  \\\n",
       "show_id                                                                 \n",
       "81145628  United States, India, South Korea, China  September 9, 2019   \n",
       "80117401                            United Kingdom  September 9, 2016   \n",
       "70234439                             United States  September 8, 2018   \n",
       "80058654                             United States  September 8, 2018   \n",
       "80125979                             United States  September 8, 2017   \n",
       "\n",
       "          release_year    rating  duration  \\\n",
       "show_id                                      \n",
       "81145628          2019     TV-PG    90 min   \n",
       "80117401          2016     TV-MA    94 min   \n",
       "70234439          2013  TV-Y7-FV  1 Season   \n",
       "80058654          2016     TV-Y7  1 Season   \n",
       "80125979          2017     TV-14    99 min   \n",
       "\n",
       "                                   listed_in  \\\n",
       "show_id                                        \n",
       "81145628  Children & Family Movies, Comedies   \n",
       "80117401                     Stand-Up Comedy   \n",
       "70234439                            Kids' TV   \n",
       "80058654                            Kids' TV   \n",
       "80125979                            Comedies   \n",
       "\n",
       "                                                description  \n",
       "show_id                                                      \n",
       "81145628  Before planning an awesome wedding for his gra...  \n",
       "80117401  Jandino Asporaat riffs on the challenges of ra...  \n",
       "70234439  With the help of three human allies, the Autob...  \n",
       "80058654  When a prison ship crash unleashes hundreds of...  \n",
       "80125979  When nerdy high schooler Dani finally attracts...  "
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('show_id', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Fernando Lebrija</td>\n",
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       "      <td>United States</td>\n",
       "      <td>September 8, 2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>TV-14</td>\n",
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       "    <tr>\n",
       "      <th>80117902</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>Fire Chasers</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 8, 2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Docuseries, Science &amp; Nature TV</td>\n",
       "      <td>As California's 2016 fire season rages, brave ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80060297</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Manhattan Romance</td>\n",
       "      <td>Tom O'Brien</td>\n",
       "      <td>Tom O'Brien, Katherine Waterston, Caitlin Fitz...</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 8, 2017</td>\n",
       "      <td>2014</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>98 min</td>\n",
       "      <td>Comedies, Independent Movies, Romantic Movies</td>\n",
       "      <td>A filmmaker working on a documentary about lov...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>70153412</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>Frasier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kelsey Grammer, Jane Leeves, David Hyde Pierce...</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003</td>\n",
       "      <td>TV-PG</td>\n",
       "      <td>11 Seasons</td>\n",
       "      <td>Classic &amp; Cult TV, TV Comedies</td>\n",
       "      <td>Frasier Crane is a snooty but lovable Seattle ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70243132</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>La Familia P. Luche</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eugenio Derbez, Consuelo Duval, Luis Manuel Áv...</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>3 Seasons</td>\n",
       "      <td>International TV Shows, Spanish-Language TV Sh...</td>\n",
       "      <td>This irreverent sitcom featues Ludovico, Feder...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80000063</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>Red vs. Blue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Burnie Burns, Jason Saldaña, Gustavo Sorola, G...</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>NR</td>\n",
       "      <td>13 Seasons</td>\n",
       "      <td>TV Action &amp; Adventure, TV Comedies, TV Sci-Fi ...</td>\n",
       "      <td>This parody of first-person shooter games, mil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70286564</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>Maron</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marc Maron, Judd Hirsch, Josh Brener, Nora Zeh...</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>4 Seasons</td>\n",
       "      <td>TV Comedies</td>\n",
       "      <td>Marc Maron stars as Marc Maron, who interviews...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70153404</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>Friends</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jennifer Aniston, Courteney Cox, Lisa Kudrow, ...</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>10 Seasons</td>\n",
       "      <td>Classic &amp; Cult TV, TV Comedies</td>\n",
       "      <td>This hit sitcom follows the merry misadventure...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2032 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             type                             title          director  \\\n",
       "show_id                                                                 \n",
       "70234439  TV Show                Transformers Prime               NaN   \n",
       "80058654  TV Show  Transformers: Robots in Disguise               NaN   \n",
       "80125979    Movie                      #realityhigh  Fernando Lebrija   \n",
       "80117902  TV Show                      Fire Chasers               NaN   \n",
       "80060297    Movie                 Manhattan Romance       Tom O'Brien   \n",
       "...           ...                               ...               ...   \n",
       "70153412  TV Show                           Frasier               NaN   \n",
       "70243132  TV Show               La Familia P. Luche               NaN   \n",
       "80000063  TV Show                      Red vs. Blue               NaN   \n",
       "70286564  TV Show                             Maron               NaN   \n",
       "70153404  TV Show                           Friends               NaN   \n",
       "\n",
       "                                                       cast        country  \\\n",
       "show_id                                                                      \n",
       "70234439  Peter Cullen, Sumalee Montano, Frank Welker, J...  United States   \n",
       "80058654  Will Friedle, Darren Criss, Constance Zimmer, ...  United States   \n",
       "80125979  Nesta Cooper, Kate Walsh, John Michael Higgins...  United States   \n",
       "80117902                                                NaN  United States   \n",
       "80060297  Tom O'Brien, Katherine Waterston, Caitlin Fitz...  United States   \n",
       "...                                                     ...            ...   \n",
       "70153412  Kelsey Grammer, Jane Leeves, David Hyde Pierce...  United States   \n",
       "70243132  Eugenio Derbez, Consuelo Duval, Luis Manuel Áv...  United States   \n",
       "80000063  Burnie Burns, Jason Saldaña, Gustavo Sorola, G...  United States   \n",
       "70286564  Marc Maron, Judd Hirsch, Josh Brener, Nora Zeh...  United States   \n",
       "70153404  Jennifer Aniston, Courteney Cox, Lisa Kudrow, ...  United States   \n",
       "\n",
       "                 date_added  release_year    rating    duration  \\\n",
       "show_id                                                           \n",
       "70234439  September 8, 2018          2013  TV-Y7-FV    1 Season   \n",
       "80058654  September 8, 2018          2016     TV-Y7    1 Season   \n",
       "80125979  September 8, 2017          2017     TV-14      99 min   \n",
       "80117902  September 8, 2017          2017     TV-MA    1 Season   \n",
       "80060297  September 8, 2017          2014     TV-14      98 min   \n",
       "...                     ...           ...       ...         ...   \n",
       "70153412                NaN          2003     TV-PG  11 Seasons   \n",
       "70243132                NaN          2012     TV-14   3 Seasons   \n",
       "80000063                NaN          2015        NR  13 Seasons   \n",
       "70286564                NaN          2016     TV-MA   4 Seasons   \n",
       "70153404                NaN          2003     TV-14  10 Seasons   \n",
       "\n",
       "                                                  listed_in  \\\n",
       "show_id                                                       \n",
       "70234439                                           Kids' TV   \n",
       "80058654                                           Kids' TV   \n",
       "80125979                                           Comedies   \n",
       "80117902                    Docuseries, Science & Nature TV   \n",
       "80060297      Comedies, Independent Movies, Romantic Movies   \n",
       "...                                                     ...   \n",
       "70153412                     Classic & Cult TV, TV Comedies   \n",
       "70243132  International TV Shows, Spanish-Language TV Sh...   \n",
       "80000063  TV Action & Adventure, TV Comedies, TV Sci-Fi ...   \n",
       "70286564                                        TV Comedies   \n",
       "70153404                     Classic & Cult TV, TV Comedies   \n",
       "\n",
       "                                                description  \n",
       "show_id                                                      \n",
       "70234439  With the help of three human allies, the Autob...  \n",
       "80058654  When a prison ship crash unleashes hundreds of...  \n",
       "80125979  When nerdy high schooler Dani finally attracts...  \n",
       "80117902  As California's 2016 fire season rages, brave ...  \n",
       "80060297  A filmmaker working on a documentary about lov...  \n",
       "...                                                     ...  \n",
       "70153412  Frasier Crane is a snooty but lovable Seattle ...  \n",
       "70243132  This irreverent sitcom featues Ludovico, Feder...  \n",
       "80000063  This parody of first-person shooter games, mil...  \n",
       "70286564  Marc Maron stars as Marc Maron, who interviews...  \n",
       "70153404  This hit sitcom follows the merry misadventure...  \n",
       "\n",
       "[2032 rows x 11 columns]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"country\"]==\"United States\"] #Filtering by country"
   ]
  }
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